ANALYSIS OF SURFACE FINISH IN DRILLING OF COMPOSITES USING NEURAL
NETWORKS
A Thesis by
Shashidhar Madiwal
B.E, Karnatak University, 1998
Submitted to
The Department of Mechanical Engineering
and the faculty of the Graduate School of
Wichita State University in partial fulfillment of
the requirements for the Degree of
Master of Science
July 2006
ANALYSIS OF SURFACE FINISH IN DRILLING OF COMPOSITES USING NEURAL
NETWORKS
I have examined the final copy of this thesis for form and content, and recommend that it be
accepted in partial fulfillment of the requirement for the degree of Master of Science with a
major in Mechanical Engineering.
______________________________________
Behnam Bahr, Committee Chair
We have read this thesis
and recommend its acceptance:
_______________________________________
Krishna Krishnan, Committee Member
________________________________________
Kurt Soschinske, Committee Member
ii
DEDICATION
To my parents and relatives
iii
ACKNOWLEDGEMENTS
I would like to thank my advisor, Dr. Behnam Bahr, for his invaluable assistance and
friendly guidance throughout my master’s program. I would also like to thank the other
committee members, Dr. Kurt Soschinske and Dr. Krishna Krishnan for their comments and
assistance in this study. I would like to thank student members Sudhama, Habib, Rupinder and
Ashkhan for their help and support. Also, I would like to express my gratitude to Dave
Richardson (Raytheon representative), Dan Thurnau (SpiritAero Systems), and Steve Shofler
(Superior Tool Services).
iv
ABSTRACT
Composite materials are widely used in the aerospace industry because of their high
strength-to-weight ratio. Although they have many advantages, their inhomogeneity and
anisotropy pose problems. Because of these properties, machining of composites, unlike
conventional metal working, needs more investigation. Conventional drilling of composites is
one such field that requires extensive study and research. Among various parameters that
determine the quality of a drilled hole, surface finish is of vital importance. The surface finish of
a drilled hole depends on speed, feed-rate, material of the work piece, and geometry of the drill
bit.
This project studied the effect of speed and feed on surface finish and also the
optimization of these parameters. Experiments were conducted based on Design of Experiment
(DOE) and qualitative verification using Artificial Neural Network (ANN). Relevant behavior of
surface finish was also studied.
In this project, holes were drilled using a conventional twist drill at different cutting
speeds (2,000 to 5,000 rpm) and feed rate was varied from 0.001 to 0.01 ipr for solid carbon fiber
laminate (composite material). The other material drilled is BMS 8-276 form 3 (toughened resin
system). Also five different drill bits were used to conduct experiments on BMS 8-276 form 3.
Speed values were 5,000, 3,000, and 2,000 rpm and feed rates were 0.004, 0.006, and 0.01 ipr.
The effect of speed, feed rate, and different drill geometries was analyzed with respect to surface
finish in the drilled composites.
v
TABLE OF CONTENTS
Chapter
Page
1. INTRODUCTION
1
1.1 Project Goal
1
2. INTRODUCTION TO DRILLING OF COMPOSITES
3
3. INTRODUCTION TO SURFACE TEXTURE
10
4. INTRODUCTION TO ARTIFICIAL NEURAL NETWORKS
15
4.1
4.2
Multi-Layered Neural Network
Back Propagation Theory
16
19
5. LITERATURE SURVEY
22
6. EXPERIMENTAL ANALYSIS
31
6.1
6.2
6.3
Application of Artificial Neural Networks
Design of Experiment
Data Comparison
32
49
63
7. RESULTS AND DISCUSSION
69
8. CONCLUSION
76
9. LIMITATIONS
77
10. FUTURE WORK
78
LIST OF REFERENCES
79
APPENDICES
82
A. Figures Showing Optimum Artificial Neural Networks
B. Tabulation Showing Output Data from Neural Network Analysis
C. Pictorial Representation of Drilled Surfaces
vi
83
84
88
LIST OF TABLES
Table
Page
1. Notations Used for Various Drill Bits
7
2. Summary of Factor Effects: S/N Ratio Analysis
26
3. Training Data for ANN (SCFL material)
34
4. ANN Input for Brad Spur
35
5. ANN Input for Double Margin
35
6. ANN Input for Conventional
35
7. ANN Input for ST1257B
35
8. ANN Input for ST1255G
36
9. Network Characteristics
36
10. Network Architecture for SCFL
36
11. Network Architecture for BMS 8-276 form 3
37
12. Surface Finish Using Artificial Neural Network for SCFL material
37
13. Surface Finish Using Artificial Neural Network for Brad Spur
37
14. Surface Finish Using Artificial Neural Network for Double margin
37
15. Surface Finish Using Artificial Neural Network for Conventional
38
16. Surface Finish Using Artificial Neural Network for ST1257B
38
17. Surface Finish Using Artificial Neural Network for ST1255G
38
18. Design of Experiments Input Data for SCFL Material
51
19. Tabulation of Surface Finish Output Using DOE for SCFL Material
54
20. Design of Experiments Input Data for Brad Spur
55
21. Tabulation of Surface Finish Output Using DOE for Brad Spur
56
vii
22. Design of Experiments Input Data for Double Margin
57
23. Tabulation of Surface Finish Output Using DOE for Double Margin
57
24. Design of Experiments Input Data for Conventional
59
25. Tabulation of Surface Finish Output Using DOE for Conventional
59
26. Design of Experiments Input Data for ST1257B
61
27. Tabulation of Surface Finish Output Using DOE for ST1257B
61
28. Design of Experiments Input Data for ST1255G
62
29. Tabulation of Surface Finish Output Using DOE for ST1255G
62
30. Experimental Data for SCFL Material
69
31. Neural Network Data for SCFL Material
69
32. DOE Predicted Data for SCFL Material
69
33. Experimental Data for Brad Spur
70
34. Neural Network Data for Brad Spur
70
35. DOE Predicted Data for Brad Spur
70
36. Experimental Data for Double Margin
71
37. Neural Network Data for Double Margin
71
38. DOE Predicted Data for Double Margin
71
39. Experimental Data for Conventional
72
40. Neural Network Data for Conventional
72
41. DOE Predicted Data for Conventional
72
42. Experimental Data for ST1257B
72
43. Neural Network Data for ST1257B
73
44. DOE Predicted Data for ST1257B
73
viii
45. Experimental Data for ST1255G
73
46. Neural Network Data for ST1255G
73
47. DOE Predicted Data for ST1255G
74
48. Output Data from DOE and Neural Network Analysis for SCFL
84
49. Output Data for SCFL with Least RMS Value, RMS = 0.169401
92
50. Output Data for Brad Spur with Least RMS Value, RMS Error = 0.169615
92
51. Output Data for Double Margin with Least RMS Value, RMS Error = 0.102468
93
52. Output Data for Conventional with Least RMS Value, RMS Error = 0.090178
93
53. Output Data for ST1257B with Least RMS Value, RMS Error = 0.115093
93
54. Output Data for ST1255G with Least RMS Value, RMS Error = 0.146705
94
ix
LIST OF FIGURES
Figure
Page
1. Cutting heads used in drilling composites
4
2. Twist drill nomenclature
4
3. Hole shape deviations
5
4. Geometry of SCFL composite coupon
6
5. Brad spur carbide drill bit
7
6. Conventional carbide drill bit
7
7. Double margin (Amamco solid carbide double margin step drill)
8
8. Spirit (ST1255G SC parabolic flute drill)
8
9. ST1257B solid carbide straight flute drill
8
10. Design of drilling fixture to hold the curve work piece
9
11. Fadal VMC20 with experimental setup
9
12. Surface texture of Component
10
13. Surface profiling method
11
14. Profile with parameters
12
15. Mitutoyo-surf-test SJ400
13
16. Surface tester with probe
13
17. Neural network processing element
16
18. Neural network structure
17
19. Damaged zone extension, D, vs drilling speed-to-feed rate ratio, Vr/Vt
23
20. Quality criteria for drilling fiber reinforced composite materials
24
21. Factor effects for surface roughness
26
x
22. Factor effects for delamination
26
23. Comparison between delamination experimental measurements and
predictions made with fusion model
28
24. Comparison between surface roughness experimental measurements and
predictions made with fusion model
28
25. Actual, predicted Ra, Rz values
29
26. Evolution of arithmetic mean roughness with cutting time
29
27. Schematic of a single neuron in a multilayered feed forward network
31
28. Neural network output (feed vs surface finish at 5,000 rpm)
39
29. Neural network output (feed vs surface finish at 4,500 rpm)
39
30. Neural network output (feed vs surface finish at 4,000 rpm)
40
31. Neural network output (feed vs surface finish at 3,500 rpm)
40
32. Neural network output (feed vs surface finish at 3,000 rpm)
41
33. Neural network output (feed vs surface finish at 2,500 rpm)
41
34. Neural network output (feed vs surface finish at 2,000 rpm)
42
35. Neural network output (feed vs surface finish at 5,000 rpm)
42
36. Neural network output (feed vs surface finish at 3,000 rpm)
43
37. Neural network output (feed vs surface finish at 2,000 rpm)
43
38. Neural network output (feed vs surface finish at 5,000 rpm)
44
39. Neural network output (feed vs surface finish at 3,000 rpm)
44
40. Neural network output (feed vs surface finish at 2,000 rpm)
45
41. Neural network output (feed vs surface finish at 5,000 rpm)
45
42. Neural network output (feed vs surface finish at 3,000 rpm)
46
43. Neural network output (feed vs surface finish at 2,000 rpm)
46
xi
44. Neural network output (feed vs surface finish at 5,000 rpm)
47
45. Neural network output (feed vs surface finish at 3,000 rpm)
47
46. Neural network output (feed vs surface finish at 2,000 rpm)
48
47. Neural network output (feed vs surface finish at 5,000 rpm)
48
48. Neural network output (feed vs surface finish at 3,000 rpm)
49
49. Neural network output (feed vs surface finish at 2,000 rpm)
49
50. Response surface for DOE-predicted output for SCFL
54
51. Response surface for DOE-predicted output for brad spur
56
52. Response surface for DOE-predicted output for double margin
58
53. Response surface for DOE-predicted output for Conventional
60
54. Response surface for DOE-predicted output for ST1257B
60
55. Response surface for DOE-predicted output for ST1255G
63
56. Surface finish values at 5,000 rpm and 0.004 in/rev
64
57. Surface finish values at 5,000 rpm and 0.006 in/rev
64
58. Surface finish values at 5,000 rpm and 0.01 in/rev
65
59. Surface finish values at 3,000 rpm and 0.004 in/rev
65
60. Surface finish values at 3,000 rpm and 0.006 in/rev
66
61. Surface finish values at 3,000 rpm and 0.01 in/rev
66
62. Surface finish values at 2,000 rpm and 0.004 in/rev
67
63. Surface finish values at 2,000 rpm and 0.006 in/rev
67
64. Surface finish values at 2,000 rpm and 0.01 in/rev
68
65. Comparison of drill bits
68
66. Neural network showing two hidden layers with five nodes each
83
xii
67. Neural network showing one hidden layers with four nodes each
83
68. Drilled surface picture at 5,000 rpm, 0.001 ipr, surface finish 1.1 µm
88
69. Drilled surface picture at 5,000 rpm, 0.01 ipr, surface finish 1.72 µm
88
70. Drilled surface picture at 2,500 rpm, 0.002 ipr, surface finish 1.18 µm
88
71. Drilled surface picture at 2,500 rpm, 0.008 ipr, surface finish 1.59 µm
88
72. Drilled surface picture at 3,000 rpm, 0.004 ipr, surface finish 0.59 µm
88
73. Drilled surface picture at 3,000 rpm, 0.01 ipr, surface finish 0.99 µm
88
74. Drilled surface picture at 2,000 rpm, 0.004 ipr, surface finish 1.05 µm
89
75. Drilled surface picture at 2,000 rpm, 0.01 ipr, surface finish 1.49 µm
89
76. Drilled surface picture at 3,000 rpm, 0.004 ipr, surface finish 2.00 µm
89
77. Drilled surface picture at 3,000 rpm, 0.01 ipr, surface finish 2.53 µm
89
78. Drilled surface picture at 2,000 rpm, 0.004 ipr, surface finish 1.65 µm
89
79. Drilled surface picture at 2,000 rpm, 0.01 ipr, surface finish 2.05 µm
89
80. Drilled surface picture at 3,000 rpm, 0.004 ipr, surface finish 0.98 µm
90
81. Drilled surface picture at 3,000 rpm, 0.01 ipr, surface finish 1.55 µm
90
82. Fiber pullout
90
83. Damaged zone
90
84. Fiber pullout
91
85. Magnified damage zone
91
xiii
LIST OF ABBREVATIONS
ANN
Artificial Neural Network
BPN
Back Propagation Network
BS
Brad Spur
CD
Conventional Drill
DM
Double Margin
DOE
Design Of Experiment
ipr
Inches per Revolution
ISO
International Standards Organization
rpm
Revolutions Per Minute
SC
Solid Carbide
SCFL
Solid Carbon Fiber Laminate
SF
Surface Finish
xiv
LIST OF SYMBOLS
Rmax
Maximum height of the irregularities
Ra
Arithmetical mean value
L
Sampling length
N
Total sampling intervals
Xi
Input to neural network
Wij
Interconnection weight
s
Combined input
f(s)
Activation function
Yj
Network output
Delj
Error to be propagated back for the jth processor in the output layer
Tarj
Target for the jth processor in the output layer
F’
Derivative of activation function
Vr/Vt
Cutting speed to feed ratio
∆D
Deviation of drilled hole diameter
fk
Roundness error
Da
Delamination factor
p
Scalar input vector
b
Scalar bias
µmtr
Micro-meter (surface finish measurement unit)
µinch
Micro-inch (surface finish measurement unit)
n
Neuron output
º
Degree
xv
CHAPTER 1
INTRODUCTION
The aerospace industry is making a major effort to utilize increasing amounts of
composite materials in order to obtain high strength-to-weight ratios. However, these materials
are easily damaged unless machining is performed properly. Several hole-production processes,
such as conventional drilling, ultrasonic drilling, laser-beam drilling, water-jet drilling, etc., have
been proposed for a variety of economic and quality reasons, but conventional drilling remains
the most preferred and adopted technique in the industry today. Due to inherent qualities such as
anisotropy and brittleness, composite materials, when subjected to drilling, exhibit damage
phenomena such as spalling, delamination, and crack formation.
The quality of a hole plays a vital role in drilling. Obtaining desired hole dimensions,
roundness, and surface finish along the length of the hole are of vital importance to the industry.
This research involved qualitative analysis of surface finish obtained during the drilling of Solid
carbon fiber laminate material specimen provided by Raytheon using a conventional drill bit, and
a second set of experiments on BMS 8-276 form 3 material provided by SpiritAero Systems
using five different drill bit types. Experimental results were verified using a neural network
technique to set up a work platform for future experiments and research.
1.1 Project Goal
Research in the field of machining of composite materials is of prime importance for the
industry.In conventional drilling used for composite materials, hole quality is the manufacturer’s
priority. Hole quality is determined by surface finish, roundness, hole diameter, etc. This
research involved the study of surface finish obtained during drilling of Solid carbon fiber
laminate (SCFL) and BMS 8-276 form 3 material.
1
Factors affecting surface finish were
determined and thus conclusions were drawn about optimum feed rate and speed of operation for
better hole quality. Many factors affect hole quality, which can be divided into controllable and
non-controllable.
Controllable Factors:
Speed
Feed Rate
Workpiece material
Drill geometry and material
Non-Controllable Factor: Machine Accuracy.
Taking the above factors into consideration, experiments were conducted to determine
the best combination of factors to obtain optimum hole quality. A methodology was adopted to
conduct these experiments in an organized fashion. This method was the Design of Experiment
(DOE). After the experiments were completed the data was analyzed using neural networks
Technique. Details regarding this technique and DOE will be explained in later chapters.
2
CHAPTER 2
INTRODUCTION TO DRILLING IN COMPOSITES
Composites can be constructed of any combination of two or more materials, whether
metallic, organic, or inorganic. Major consistent forms used in composite materials are fibers,
particles, laminate or layers, flakes, fillers and matrixes.
Conventional metal-cutting drill tips were designed so that the tip heating the metal
would provide the plastic flow needed for efficient cutting. Since composite materials can not
tolerate this heat, production must be slowed down to keep the heat as low as possible. Drill
designs had to abandon cutting tips with negative rakes and wide chisel points because the drill
scrapes the material and causes it to resist penetration by the drill tip. The operator must exert
pressure to drill the hole, and pressure causes heat buildup [2]. Modified drill geometries were
used to counter the problem. Typical drills are as shown in Figure 2.1. The use of conventional
twist drill is also popular in drilling of composites. Nomenclature of a twist drill is as shown in
Figure 2.2.
The best way to analyze the drilling operation in composites is to examine the chips,
which ideally are dry and easily moved. If the speed of the cutting tool is too high, heat will
make the resin sticky and produce a lumpy chip; if the cutting edge is scraping and not cutting
the plastic, the chips will be large and flaky. Either type will eventually clog any evacuation
system [2].
During hole fabrication in composites, shape deviations occur. Hole shape deviations
define the difference between the shape of the machined hole and the geometrical shape required
by the drawing. Hole shape deviates with respect to roundness (oval) and the profile from the
mean line as shown in Figure 2.3. An oval occurs as either a single or a multiple.
3
Errors of a profile cross section are roughness, waviness, and lay. Vibration in the system
machine tool work piece is the reason that surface waviness occurs [10].
Figure 2.1. Cutting heads used in drilling composites: (a) solid shank drill (b) drill guide system
(c) fluted twist drill [2].
Figure 2.2. Twist drill nomenclature [16].
4
Parameters for Grading Hole Quality
Figure 2.3. Hole shape deviations: (a) Theoretical view (b) Actual view [10].
Other forms of drilling composites commonly practiced are as follows:
1.
Laser drilling
2.
Ultrasonic drilling
3.
Abrasive drilling
5
Experiments conducted for this research used Solid Carbon Fiber Laminate as the
workpiece material. Each workpiece was cut to a size of a one inch by six inches from a larger
specimen that had a curvature of approximately eighty-three inches, as shown in Figure 2.4.
Figure 2.4. Geometry of SCFL composite coupon.
The drill bit used for these experiments was as follows:
Type
Two flute drill
Drill Diameter
0.25”
Material
Point Angle
Clearance
Carbide
135°
12°
The geometry of the BMS 8-276 form 3 coupon was simple- a flat piece seven inches
long length and approximately half inch thick.
Technical details of the five distinct drill bits used were not available; hence, the
provider’s name or the commercial name of the bits were used for experimentation and analysis.
The five drill bits used are as shown in Table 2.1 along their respective notations. The pictures
showing the five drill bits are in form of Figures 2.5, 2.6, 2.7, 2.8 and 2.9. There was a special
fixture designed for holding the composite workpiece which is shown in Figure 2.10. The NC
machine along with the fixture is shown in Figure 2.11.
6
TABLE 2.1
NOTATIONS USED FOR VARIOUS DRILL BITS
Drill Bit Type
Notation Used
ST1255G solid carbide parabolic flute drill
ST1255G
ST1257B solid carbide straight flute drill
ST1257B
Amamco solid carbide double margin step drill
DM
Brad spur carbide drill bit
BS
Conventional carbide drill bit
CD
Figures 2.5 to 2.9 show the five drill bits.
Figure 2.5. Brad spur carbide drill bit
Figure 2.6. Conventional carbide drill bit
7
Figure 2.7. Amamco solid carbide double margin step drill
Figure 2.8. ST1255G SC parabolic flute drill
Figure 2.9. ST1257B Solid carbide straight flute drill.
8
Figure 2.10. Design of Drill fixture to hold the curve work piece
Figure 2.11. Fadal VMC20 with experimental setup
9
CHAPTER 3
INTRODUCTION TO SURFACE TEXTURE
One of the principal design considerations for highly stressed components will be, the
surface condition produced during manufacturing. Surface technology describes details and
evaluates the condition of both the surface and the surface layers of manufactured components.
Surface texture has been extended to include the surface integrity, thereby including the
influence of the outermost boundary of a component, as well as those at the outermost layers
which differ measurably from the base material [1].
Definitions Related to Surface Quality
Figure 3.1. Surface Texture of Component [1].
Waviness: The recurrent deviation from an ideal surface and of a relatively large
wavelength as seen in Figure 3.1. Such deviations generally result from deflections of the tool,
workpiece, or machine vibration or warping, and means that the workpiece and tool should be
held rigidly with as little overhang as possible in order to minimize wariness.
Lay: The direction of the predominant surface pattern produced by feed marks as shown
in Figure 3.1.
10
Roughness: The finely spaced irregularities or irregular deviations as shown in Figure
3.1. Roughness is affected by tool shape and feed as well as machining conditions. The figure
shown is an example from ISO/R468. Roughness is described by the maximum height of the
irregularities, Rmax, and the arithmetical mean value, Ra. Rmax is the maximum peak-to-valley
height within the sampling length. Ra is the average of the numerical deviations from the mean
line of the surface within the sample length. The relation between Ra and Rmax for triangular
irregularities with an approximation is,
Ra ≈ Rmax / 4
Profiling: A means of measuring the profile of a surface.
(3.1)
This results in a two-
dimensional graph of the shape of the surface in the sectioning plane created by the profiling
instrument. The most common type of profiling instrument draws a stylus across the surface and
measures its vertical displacement as a function of position as shown in Figure 3.2.
Figure 3.2. Surface profiling method [1].
Surface Texture Measurement
The most prevalent measuring technique for surface texture employs a mechanicalelectronic device, that provides a readout indicating the roughness of the surface profile taken
during the passage of a small radius stylus over a short straight line path on the surface. The most
11
common diamond stylus has a 0.0004-inch radius and usually is used with a 0.030-inch (0.08
mm) cutoff width. The total stylus travel is usually twenty to sixty times the cutoff width, with
the electronic circuitry continuously averaging the readings over the set cutoff width. These
instruments can read average roughness, Ra, Peak count or other roughness designations
depending on the particular instrument design [1]. Details regarding this instrument will be
discussed in detail in the following sections.
Average Roughness (Ra): The area between the roughness profile and its mean line on
the integral of the absolute value of the roughness profile height over the evaluation length.
Equation 3.2 gives the mathematical relation for Ra.
Ra = 1/L 0∫L │r(x)│dx
(3.2)
When evaluated from the digital data, the integral is normally approximated by the trapezoidal
rule which is given by equation 3.3.
Ra = 1/N 1ΣN │rn│
(3.3)
Graphically, the average roughness is the area between the roughness profile and its
center line divided by the evaluation length. Refer to Figure 3.3.
Figure 3.3. Profile with parameters [1].
The Mitutoyo Surf Test SJ400 was used for measuring the surface profile in these
experiments. The Surf Test 400 (Figure 3.4) consists of various precision parts and should be
12
treated with utmost care. The instrument is sensitive to vibration, shock, and heat. When the
instrument is used for measuring the surface finish, it should always be placed on a measuring
desk.
Surface Tester
Figure 3.4. Mitutoyo Surf Test SJ400.
Figure 3.5. Surface tester with probe.
The machine should be calibrated before it is used for taking measurements. A precision
reference specimen should be used for calibration. If the instrument-displayed Ra value does not
agree with that of the specimen, then the gain volume of the drive should be adjusted to agree
with it. Once the instrument has been attached completely and calibrated, the nosepiece (Figure
3.5) is placed on the surface of the workpiece, and the zero adjust knob is rotated for proper
indication. Before taking the reading of surface finish, the following must be determined and set:
Parameter: Ra (Average Surface roughness)
Range: Based upon the estimated roughness
Cut off length: To determine the evaluation length
13
Generally the workpiece surface to be measured is not uniform in roughness and varies
depending on the portion or portions to be measured so that the population mean of surface
roughness can be obtained. For the direction in which the measurement is made, the workpiece
surface must be set so that the maximum value of surface roughness is obtained.
For measuring Ra, an evaluation length is not always favorable, because the measurement
of a machined surface having a series of lays at regular intervals, such as those from shaping or
milling, it is not rare that the peaks or valleys of less than five only are included for evaluation by
a recommended length, resulting in a false measurement. To solve this problem, it is
recommended that an evaluation length of at least six times longer then the interval of lays is
used. This eliminates waviness, thus making the evaluation length longer and measurement result
better.
14
CHAPTER 4
INTRODUCTION TO ARTIFICIAL NEURAL NETWORKS
Neural systems can learn to approximate any function and behave like associative
memories using exemplar data that is representative of the desired task. Neural systems estimate
a function without requiring a mathematical description of how the output functionally depends
on the input. They learn from the input-output data samples.
An artificial neural network (ANN) consists of numerous simple processing units or
neurons that can be modified to realize a desired behavior. Neural networks are “trained” by
being given a series of examples of correct responses, and then the connections between
processors are strengthened or weakened according to the level of success in reproducing what is
wanted. The network is never given an explicit body of rules to follow-its program is contained
in the strengths and weaknesses of different links within it.
Neural Network Background
In general, neural networks can be thought of as a collection of interconnected parallel
processing elements, in which knowledge possessed by the network is represented by the
strength of interconnections between processors. The strength of an interconnection is denoted
by a numerical quantity, referred to as an interconnection weight. The interconnections
themselves can be thought of as unidirectional communication links, which provide a means of
transmitting input/output signals between processing elements. The interconnection weight
modifies the signal (usually by multiplication) to reflect the knowledge stored along the data
path. The processing element, illustrated in Figure 4.1 may have any number of input paths but
only one output (interconnection links). The input, Xi, can originate from the output paths of
other processing elements or themselves, in the case of feedback or from external sources.
15
Figure 4.1. Neural network processing element.
Inputs are modified by the interconnection weights (Wij) and combined to from a single
result (typically by summing), s. The combined input, s, is then modified by an activation
function, f(s), which can be as simple as a threshold function, for which output is produced only
if the combined inputs exceed a given level, or as a complex as a nonlinear continuous function
such as the Sigmoid or Hyperbolic Tangent, which generates an output, Yj, proportional to the
combined input. The activation function response is transmitted along the output path. Output
signals may become the input to other processing elements or sent to external sources for
interpretation.
A neural network consists of a number of processing elements joined together. Its
architecture generally resembles layers of processing elements with full or random connections
between successive layers as illustrated in Figure 4.2. The first layer is usually an input buffer
where the data is presented to the network; the last layer is an output buffer that holds the
networks response. The layers between the input and output buffer are called hidden layers [15].
16
Figure 4.2. Neural network structure.
4.1 Multilayer Feed Neural Network
A neural network is a parallel-processing architecture in which knowledge is represented
in the form of weights between a set of highly connected processing elements. Analog ANNs
have demonstrated the capability to perform nonlinear pattern association between input and
output variables. Development of the back propagation algorithm has resulted in renewed
interest in this area. The back propagation network (BPN) algorithm computes weights in the
network in order to minimize the output error in a least-squared sense. Robustness and
generalization capabilities make it an attractive alternative to conventional classifiers [11].
A multilayer BPN consists of multiple layers of processing elements that are
interconnected by weighted arcs. Each element sums the product of its inputs and the connection
weights from the previous layer, and then limits it by a nonlinear thresholding function. The
sigmoidal function defined as:
f(s) = 1/ (1+exp(-s))
(4.1)
This threshold function has been a choice in many applications. The next layer uses
outputs from the processing elements of the previous layer and computes the weighted sum
limited by the thresholding function, and so on. The training stage of the BPN uses errors
17
propagated from the output layer nodes to lower-level nodes to adjust weights. The local weight
corrections are performed using the Norm-Cum-Delta learning rule.
Network Operations
There are two distinct phases in network operations: learning and recall. Because the
recall operation is part of the back propagation algorithm used in the learning process, this will
be described first.
Recall: Compared to learning, recall is relatively simple. It begins by presenting the input
layer with an input pattern. Input signals, now representing output from the input buffer, are
broadcast to the hidden layer processors through the connection weights, Wji. Signals are
multiplied by the weights and summed by the hidden-layer processors (threshold is also included
in the summation). The summed inputs are passed through the activation function f(s) to yield an
output signal, Yj, that propagates through the weights Wji to the next layer (output). There, each
processor receives the weighted output of every element in the previous (hidden) layer. For a
network of multiple (hidden) layers, the operation is repeated layer by layer. The two equations
used in the recall mode are shown in equations (4.2) and (4.3).
General form of the recall operation:
Summation:
N
Sum, Sj= ∑i
Xi * Wji
(4.2)
where Sj is the summed weighted signals for the jth processor in the current layer, Xi is the
output from the ith of previous layer, and Wji is the weight from the ith processor of the
previous layer to jth processor of current layer.
Output Signal:
Yj=f(s) = 1
(1 + exp(− Sj ))
18
(4.3)
where Yj is the output and summed weighted input of the jth processor in the current layer
4.2 Back Propagation Theory
Learning:
In essence, the back propagation algorithm teaches the network by presenting a known
input pattern and having the network calculate an output response using the current set of
weights and thresholds. The output pattern, or “Target,” and an error are computed. The error is
propagated back through the network to adjust the weights and thresholds to minimize error
between the two patterns.
This two-step learning process of feeding forward and propagating the error back is
repeated for every pattern in the training set until the network converges and responds with the
desired patterns (training sets include input pattern and desired output pattern).
The first step in the back propagation algorithm has already been described in the section
on recall. The second step in the algorithm begins by subtracting the output of each processor
(output layer) from the corresponding “Target” patterns to produce a difference (error). The
value is then multiplied by the derivative activation function, evaluated at the current net value of
the output processor to produce an error value (Del) for that particular processor. Del is
computed by equation (4.4).
Delj = (Tarj - Yj)* F’ (Sj)
(4.4)
where Delj is the error to be propagated back for the jth processor in the output layer, Sj is the
summed weighted signals for the jth processor in the current layer, Yj is the output for the jth
processor in the output layer, Tarj is the Target for the jth processor in the output layer, and F’ is
the derivative of the activation function (Sigmoid).
The derivative of Sigmoid equals
19
F’(s) = F(s)(1-F(s))
(4.5)
The next step is the adjustment of the weights between the output and hidden layers. This
is done in two steps. The first step determines the actual amount of weight change, including the
momentum term that enhances network convergence. The second step actually changes the
interconnection weight. The two relationships are shown by equations (4.6) and (4.7).
Del Wji(n+1) =
Φ * DelWji(n)+ β *Delj*Yi
(4.6)
where DelW ji(n+1) is the current change in weight W ji at the next step n+1, DelW ji(n) is the
previous change in weight W ji at step n, initially set to zero, Delj is the error to be propagated
back from the jth processor in noutput layer, Yi is the output for the ith processor in the lower
layer connected to the weight in question, and O is the processor gain level set from 0 to 1.
New Weight Value:
Wji(n+1) = Wji(n) + DelWji(n+1)
(4.7)
where W ji(n+1) is the new weight W ji value at step n+1 (after adjustment), and W ji(n) is the
previous weight W ji at step n(before adjustment).
The momentum term changes the weight according to the previous weight changes. This
has a tendency to filter out high-frequency variations in the error surface. It has been observed
that values around 0.9 for both gain and momentum provide good converging rates with an
acceptance level of oscillation.
Next, the interconnection weights associated with the hidden layer are adjusted. The
training process used earlier will not work here, since hidden layers have no target. Therefore,
the solution lies in propagating the output error back through the network layer by layer
adjusting weights at each layer. Hence, each processor in the hidden layer receives the Del error
signal which is the weighted sum of the preceding (output) layer’s error signal. The hidden
20
processor passes this error on to all processors in the next level to which it connects, stopping
only when the next lower layer is the input buffer.
Thus, the propagation error Del for a hidden layer processor is produced by the summing
the products of each processor’s Dels in the preceding (output) layer and the interconnection
weight joining the two processors and then multiplying by the derivative of the activation
function evaluated at the current processor’s net level (calculated earlier during the feed forward
process and stored). This calculation is expressed in the equation form as
N
Deli = F’ (sumj) * ( ∑k Del k * Wkj)
(4.8)
where Delj is the error to be propagated back for the jth processor in the hidden layer, Delk is the
error from the kth processor in the previous layer, Sumj is the summed weighted from the kth
processor of the previous layer to jth processor of the current layer, and F’is the derivative of the
activation function (Sigmoid).
After finding the hidden layer’s Dels, the weights associated with the processors must be
adjusted by applying the equation used earlier. The process is repeated for every processor in
each layer, from output to input, including threshold weights. The back propagation algorithm is
applied to each pattern set, input and target, for all pattern sets in the training set. Because the
learning process is iterative, the entire training set will have to be presented to the network
repeatedly, until the global error reaches a minimum acceptable value [15].
21
CHAPTER 5
LITERATURE REVIEW
Over the years, less research has been conducted to determine the surface finish obtained
during the drilling of composites. From various research papers it can be concluded that the
quality of a hole is determined by the hole roundness, burr height, fiber pullout, and
delamination. Although surface finish is a important factor in determining hole quality, very few
reviews mention about this factor.
In machining composite parts, a finish comparable to metals cannot be achieved because
of inhomogeneity and anisotropy of material. Although some new technologies can attain
satisfactory results in terms of cut quality and operational times, their industrial applicability is
strongly limited by machine cost, and their effectiveness is confined to specific materials and/or
operations.
Fiber pullout and fuzzing, intralaminar cracks, and delamination are typical damage
modes occurring in a composite material subjected to drilling. It is expected that such damage
will result in poor mechanical properties of the material around the hole. A good finish may be
of considerable importance, especially when the edges of the hole are designed to carry a
concentrated load, such as in riveted and bolted joints. In spite of this, little research effort has
been expended to determine the optimum cutting parameters for obtaining a satisfactory hole
quality in drilling composite materials by conventional methods [9].
The problem of optimizing of drilling parameters in machining glass fiber-reinforced
plastics (GFRP) occurred where flat panels obtained by hand lay-up and reinforced with mat and
woven roving were machined under different drilling conditions by using a conventional high
speed steel tool. Two polyester resins were adopted as matrix systems. Because of lack of
22
standards to quantitatively evaluate the damage, the width of the damage zone was conveniently
assumed as an index of drilling quality. Experimental results showed that this quality index was
strongly affected by the cutting speed to feed ratio, Vr/Vt; in particular, large damaged zones
were observed when low Vr/Vt values were adopted. When Vr/Vt was increased, the extent of
the fractured zone reached a minimum, beyond which it remained constant as shown in Figure
5.1. Both the width of the damaged zone and minimum Vr/Vt ratio resulting in the minimum
damage width were found to be negligibly influenced by matrix type [8].
Figure 5.1. Damaged zone extension, D, vs drilling speed to feed rate ratio, Vr/Vt [8].
Parameters for grading hole quality in drilling of composites were suggested by Koberic
and Miskovic [10]. They suggested an assessment of various features involved in the drilling
process. The assessment of the form deviations in view of dimensional accuracy is based on the
deviation of the drilled hole diameter ∆D against the tool diameter (as measure for the required
diameter). Regarding accuracy of the shape the roundness error (fk) is applied which describes
the deviation from the ideal roundness as shown in Figure 5.2. Due to low wall thickness of the
23
components and depths of drilling, cylindrically deviation becomes a significant criterion of
quality [10].
Figure 5.2. Quality criteria for drilling fiber reinforced composite materials [10].
The assessment of the form deviations in view of dimensional accuracy is based on the
deviation of the drilled hole diameter ∆D against the tool diameter (as measure for the required
diameter). Regarding accuracy of the shape, the roundness error (fk), which describes the
deviation from the ideal roundness is applied as shown in Figure 5.2. Due to low wall thickness
of the components and depths of drilling, cylindrical deviation becomes a significant criterion of
quality. All these errors are caused by the behavior of the tool, particularly the misalignment of
the axes well as rigidity. A significant criteria of quality are material damages, mainly in the
surface layers. Characteristic shapes of damage are edge chipping and spalling that appear in
composites with glass and carbon fibers, and a fuzzing characteristic in agamid fibers.
Delamination that represents separation of surface layers of the material upon entrance of the exit
24
of the tool, differs from crack formation within the working piece, which, in the case of
laminates, are often interlaminar. Usually a measure of this error, the maximally damaged
surface vertical to the drilling axis, is taken [10]. In addition to surface roughness and roundness
error they also suggest using the fuzzing parameter and delamination parameter as a measure of
hole quality.
Other related research involving drilling of composite material and testing of surface
roughness was presented by Enemuoh [12]. Material used in this research consisted of a
magnamite graphite fiber-reinforced polyether ether ketone (AS4/PEEK) composite. Cutting
speed and drill tool material were reported to have the primary effect on surface roughness and
delamination during drilling of this material.
This research involved a method for characterization of the machinability of composite
materials. The method was based on the parametric analysis of the drilling process using the
Design of Experiment approach. It aimed at quantifying the effect of cutting speed, feed rate,
tool material and tool geometry on delamination, surface roughness, and thrust force during
drilling of carbon fiber-reinforced composites.
The analysis of the data obtained was conducted using Analysis of Variance (ANOVA).
The summary of factor effects is shown in Table 5.1. Clearly, tool material has the strongest
effect on machinability responses measured by surface roughness and delamination of holes
drilled in AS4/PEEK. Additionally, cutting speed is the most significant factor affecting the
surface roughness accounting for 45 percent of the total effect. Unlike with the machining of
metals, feed rate has only a minor influence on surface roughness of machined composites. The
type of the tool material has the most significant effect on delamination, a 60 percent
contribution. The cutting speed and drill point angle are the next highest contributors, almost 15
25
percent for each factor, refer to Figure 5.3. Finally, delamination is least affected by feed rate
with its ten percent contribution, refer to Figure 5.4 [12].
TABLE 5.1
SUMMARY OF FACTOR EFFECTS: S/N RATIO ANALYSIS [12]
Figure 5.3. Factor Effects for
Surface Roughness [12].
Figure 5.4. Factor Effects for
Delamination [12]
Another study presented by Enemuoh and El-Gizaway [6] involved the use of neural
network based sensor fusion for prediction of delamination and surface roughness in composite
26
drilling. They suggested that numerous factors influence the quality characteristics, surface finish
(Ra) and delamination (Da) during drilling operations. Rather than specific cutting condition
values, a more appropriate neural network was designed using a range of drilling parameters and
conditions. This study was restricted to two drilling parameters (feed rate and cutting speed) and
two drilling conditions (tool material and tool geometry). The two sensors used were thrust force
(z-component) and acoustic emission (z-component). It has been shown that these sensors
provide relevant data that correlate with the aforementioned drilling conditions. Cutting speed,
tool material, tool point angle, and feed rate comprise a Taguchi Orthogonal Array. The values
selected for the experiment were chosen so as not to obscure the influence of any factor on the
neural network. Additionally, each of the nine array experiments were repeated in order to
evaluate the variability associated with a given test condition and to reduce the experimental
errors. The experimental responses in this design include drilling thrust force, acoustic emission,
delamination, and surface roughness of the drilled holes. These data, which comprise the mean
of the repetitive measurements were used as examples for training the artificial neural network.
The conclusion drawn was to show the efficiency of the neural network model that they
employed, refer to Figure 5.5 and 5.6.
They did not show any relative analysis between the factors governing the experiment
and the quality of the holes obtained.
Elanayar [11] used neural networks to monitor tool wear and surface roughness for
automation. In his study, he extracted tool ware and surface finish data by using three
components of force signals using neural networks. This is because cutting forces are related to
the state of tool wear. He employed a three-layer back propagation neural network for the
27
monitoring of system conditions. The networks were first trained using the back propagation
algorithm with a known set of measured data at the training stage [11].
Figure 5.5. Comparison between delamination experimental measurements and predictions made
with fusion model [6].
Figure 5.6. Comparison between surface roughness experimental measurements and predictions
made with fusion model [6].
Off-line measurements were taken for tool wear and surface finish at pre-determined
intervals. A hierarchical network architecture was chosen to represent the physical relation of
variables and reduce network sizes. After training was completed, the networks were exposed to
external stimuli, that is cutting forces, in order to extract process conditions. The results
demonstrated the feasibility of using neural networks to represent ill-defined relationships
between tool wear, surface finish, and cutting forces, refer to Figure 5.7. This work showed that
28
the ability to represent nonlinear relationships between patterns can be effectively used for
monitoring purposes [11].
Figure5.7. Actual, Predicted Ra, Rz values [11].
Figure5.8. Evolution of Arithmetic mean roughness with cutting time [7].
Davim and Baptista [7], conducted experiments to measure the cutting force, tool wear
and surface finish in metal matrix composites. They carried out the experiments with three
different cutting speeds and four different feed rates. The measured average roughness value Ra
varied between 0.25 and 1.25 µm [7]. As could be expected for geometrical reasons, the increase
29
of the feed determined the increase of Ra values, refer Figure 5.8. For the same feed, the
increase of the cutting speed should diminish these values, as usually observed in machining
operations. However, the present results did not agree with this assertion [7].
30
CHAPTER 6
EXPERIMENTAL ANALYSIS
Neuron Model
A Neuron with scalar input vector “p” and a scalar bias “b” is shown in Figure 6.1.
Inputs
1st layer
2nd layer
P1
n (Output)
P2
P1(w1,1)
P2(w2,2)
f
∑
n
b
Figure 6.1. Schematic of a single neuron in a multilayered feed forward network.
The transfer function net input “n,” again a scalar given by equation (6.1), is the sum of the
weighed input “p” and the bias “b.” This sum is the argument of the transfer function “f.”
n = i=1∑P WipPi + b
(6.1)
The weights “w” and “b” are both adjustable scalar parameters of the neuron. The central
idea of neural networks is that such parameters can be adjusted so that the network exhibits some
desired or interesting behavior. Thus, we can train the network to do a particular job by adjusting
the weight or bias parameters, or perhaps the network itself will adjust these parameters to
achieve some desired end.
31
Back Propagation Theory
This algorithm basically is designed to minimize E, the sum of squared errors between
the estimated networks outputs (Oij) and the desired outputs (Tij) over the N exemplars in the
training data set, each of them containing M outputs. The performance function of resilient back
propagation algorithm is illustrated as
E = 1/2( i=1∑N j=1∑M (Oij – Tij)2 )
(6.2)
Multilayered networks typically use sigmoid transfer functions in their hidden layers.
Sigmoid transfer functions are characterized by the fact that their slope must approach zero as
the input increases. This causes a problem when using the steepest descent to train multilayered
networks with sigmoid functions. This is because the gradient can have a very small magnitude
and, therefore, cause small changes in weights and biases, even though the weights and biases
are far from their optimal values.
6.1 Application of Artificial Neural Networks
Based on the DOE conducted for respective inputs that is seven speeds and six feed rates,
and considering three replicates a total of 126 experimental runs were performed for SCFL
material.
For the BMS 8-276 form 3 material, five different drill bits were used to conduct
experiments and obtain surface finish data. For each drill bit, three speeds and three feed rates
were considered. Three replicates were considered for each experimental set. After obtaining
surface finish data from each replicate, the output data was subjected to both ANOVA and
Neural network analysis. Neural network analysis was conducted in two phases. The learning
phase is where data is fed to the network for training purposes. The data set used for training
included all cutting speed values and only certain feed rates as shown in the Table 6.1. The
32
learning data for BMS 8-276 form 3 material and five different drill bits is shown in Tables 6.2
to 6.6. The test phase is where the network is capable of generating an output with respect to the
developed function. The test data was selected at all speed values and at 0.002, and 0.008 ipr as
feed rates for SCFL material. For the BMS 8-276 form 3 material the test data was run at 0.006
ipr as feed rate. A set of output data obtained by conducting drilling experiments was compared
to the output obtained from the network to achieve a RMS error value. The different
configurations were tested until the RMS error value was minimum.
The analysis using the network was conducted using norm-cum-delta as the learning rule
and sigmoid as the transfer function. The network characteristics used for both type of material is
shown in Table 6.7. The network architecture for SCFL material and BMS 8-276 form 3 material
are shown in Table 6.8 and 6.9 respectively. Data was fed to the network with the following
configurations: single hidden layer with combinations of five nodes, that is single node through
five nodes. Two hidden layers with combinations of five nodes, that is nodes one through five.
Figures A.1 and A.2 show the optimum combination networks are shown in Appendix A.
The number of runs conducted for the learning process totaled 200,000. The output data
obtained after the test run was tabulated, and using the output test data the RMS error value was
calculated. The optimum calculated values are depicted along with the RMS error values for
different configurations in Table B.1 to B.7 in Appendix B.
The least RMS value for SCFL material was with a single hidden layer four node configuration.
For this optimum network the output for the entire set of values was determined and it is
tabulated in the Table 6.10. Similarly for the different drill bits used the optimum network was
one with two hidden layers and five nodes each. The entire output data set for the five different
drill bits is shown in Tables 6.11 to 6.15.
33
TABLE 6.1
TRAINING DATA FOR ANN (SCFL)
SPEED
5000
5000
5000
5000
5000
5000
5000
5000
5000
5000
5000
5000
4500
4500
4500
4500
4500
4500
4500
4500
4500
4500
4500
4500
4000
4000
4000
4000
4000
4000
4000
4000
4000
4000
4000
4000
3500
3500
3500
3500
3500
3500
FEED
0.001
0.001
0.001
0.004
0.004
0.004
0.006
0.006
0.006
0.01
0.01
0.01
0.001
0.001
0.001
0.004
0.004
0.004
0.006
0.006
0.006
0.01
0.01
0.01
0.001
0.001
0.001
0.004
0.004
0.004
0.006
0.006
0.006
0.01
0.01
0.01
0.001
0.001
0.001
0.004
0.004
0.004
S.FINISH
1.1
0.96
1.04
1.32
1.29
1.38
1.46
1.56
1.6
1.72
1.8
1.68
0.98
0.7
1.06
1.32
1.19
1.45
1.49
1.57
1.51
1.59
1.71
1.85
1.03
0.87
1.12
1.24
1.29
1.36
1.42
1.5
1.58
1.77
1.89
2.01
0.94
1.02
0.86
1.13
1.24
1.19
34
SPEED
3500
3500
3500
3500
3500
3500
3000
3000
3000
3000
3000
3000
3000
3000
3000
3000
3000
3000
2500
2500
2500
2500
2500
2500
2500
2500
2500
2500
2500
2500
2000
2000
2000
2000
2000
2000
2000
2000
2000
2000
2000
2000
FEED
0.006
0.006
0.006
0.01
0.01
0.01
0.001
0.001
0.001
0.004
0.004
0.004
0.006
0.006
0.006
0.01
0.01
0.01
0.001
0.001
0.001
0.004
0.004
0.004
0.006
0.006
0.006
0.01
0.01
0.01
0.001
0.001
0.001
0.004
0.004
0.004
0.006
0.006
0.006
0.01
0.01
0.01
S.FINISH
1.37
1.43
1.55
1.78
1.62
1.59
0.76
0.89
1.01
1.22
1.15
1.2
1.36
1.45
1.51
1.68
1.76
1.9
1.1
0.95
0.87
1.21
1.14
1.29
1.39
1.45
1.31
1.64
1.73
1.59
0.96
1.05
0.92
1.23
1.29
1.19
1.44
1.39
1.49
1.71
1.82
1.63
TABLE 6.2
TABLE 6.4
ANN INPUT FOR BRAD SPUR
SPEED
5000
5000
5000
5000
5000
5000
3000
3000
3000
3000
3000
3000
2000
2000
2000
2000
2000
2000
FEED
0.004
0.004
0.004
0.01
0.01
0.01
0.004
0.004
0.004
0.01
0.01
0.01
0.004
0.004
0.004
0.01
0.01
0.01
ANN INPUT FOR CONVENTIONAL
SPEED
5000
5000
5000
5000
5000
5000
3000
3000
3000
3000
3000
3000
2000
2000
2000
2000
2000
2000
S.FINISH
1.35
1.37
1.35
2.05
2.01
2.03
1.15
1.15
1.17
1.56
1.54
1.55
1.07
1.06
1.05
1.49
1.45
1.48
TABLE 6.3
FEED
0.004
0.004
0.004
0.01
0.01
0.01
0.004
0.004
0.004
0.01
0.01
0.01
0.004
0.004
0.004
0.01
0.01
0.01
S.FINISH
0.7
0.72
0.73
1.2
1.22
1.23
0.59
0.6
0.62
1
0.99
0.99
0.56
0.57
0.58
0.91
0.95
0.96
TABLE 6.5
ANN INPUT FOR DOUBLE MARGIN
SPEED
5000
5000
5000
5000
5000
5000
3000
3000
3000
3000
3000
3000
2000
2000
2000
2000
2000
2000
FEED
0.004
0.004
0.004
0.01
0.01
0.01
0.004
0.004
0.004
0.01
0.01
0.01
0.004
0.004
0.004
0.01
0.01
0.01
ANN INPUT FOR ST1257B
S.FINISH
2.23
2.25
2.25
2.73
2.7
2.75
2
2.01
2.03
2.53
2.5
2.52
1.93
1.95
1.97
2.33
2.35
2.33
SPEED
5000
5000
5000
5000
5000
5000
3000
3000
3000
3000
3000
3000
2000
2000
2000
2000
2000
2000
35
FEED
0.004
0.004
0.004
0.01
0.01
0.01
0.004
0.004
0.004
0.01
0.01
0.01
0.004
0.004
0.004
0.01
0.01
0.01
S.FINISH
1.99
1.99
1.98
2.95
2.96
2.98
1.72
1.72
1.75
2.35
2.36
2.35
1.68
1.69
1.65
2.05
2.01
2.02
TABLE 6.6
ANN INPUT FOR ST1255G
SPEED
5000
5000
5000
5000
5000
5000
3000
3000
3000
3000
3000
3000
2000
2000
2000
2000
2000
2000
FEED
0.004
0.004
0.004
0.01
0.01
0.01
0.004
0.004
0.004
0.01
0.01
0.01
0.004
0.004
0.004
0.01
0.01
0.01
S.FINISH
1.15
1.19
1.2
1.84
1.88
1.85
1.05
1.02
0.98
1.55
1.55
1.54
0.95
0.98
0.96
1.35
1.36
1.35
TABLE 6.7
NETWORK CHARACTERISTICS
Artificial Neural Network
Characteristics
Value
Paradigm
Learn Function
Transfer Function
Code
Learning Cycle
Back Propagation
Norm-cum-Delta
Sigmoid
Binary
200000
TABLE 6.8
NETWORK ARCHITECTURE FOR SCFL
Architecture
Layer
No of Inputs
No of Hidden
No of Outputs
Processing Elements
2
1 (Nodes 4)
1
36
TABLE 6.9
NETWORK ARCHITECTURE FOR BMS 8-276 FORM 3
Architecture
Layer
No of Inputs
No of Hidden
No of Outputs
Processing Elements
2
2 (Nodes 5)
1
TABLE 6.10
SURFACE FINISH USING ARTIFICIAL NEURAL NETWORK FOR SCFL MATERIAL
Speed(rpm)
5000
4500
4000
3500
3000
2500
2000
Feed (inch/rev)
0.001
0.002
1.030617 1.116811
1.016725 1.101726
1.003551 1.086215
0.991108 1.070471
0.979393 1.054718
0.968393 1.03919
0.958078 1.024099
0.004
1.290615
1.273175
1.256279
1.240016
1.224458
1.209658
1.195652
0.006
1.474671
1.456223
1.438014
1.420166
1.402793
1.385999
1.369872
0.008
1.649249
1.63311
1.616943
1.600985
1.585451
1.570503
1.556249
0.01
1.820094
1.804131
1.787775
1.771111
1.754237
1.737263
1.720307
TABLE 6.11
SURFACE FINISH USING ARTIFICIAL NEURAL NETWORK FOR BRAD SPUR
Speed(rpm)
5000
3000
2000
Feed(inch/rev)
0.004
0.006
1.415239 1.598431
1.12815
1.280777
1.019406 1.14388
0.01
1.943253
1.637637
1.465196
TABLE 6.12
SURFACE FINISH USING ARTIFICIAL NEURAL NETWORK FOR DOUBLE MARGIN
Speed(rpm)
5000
3000
2000
Feed(inch/rev)
0.004
0.006
2.254497 2.423081
2.019475 2.167738
1.928052 2.052708
37
0.01
2.718515
2.496588
2.362576
TABLE 6.13
SURFACE FINISH USING ARTIFICIAL NEURAL NETWORK FOR CONVENTIONAL
Speed(rpm)
5000
3000
2000
Feed(inch/rev)
0.004
0.006
0.739995 0.891971
0.599187 0.728379
0.543877 0.65496
0.01
1.177708
1.027552
0.93924
TABLE 6.14
SURFACE FINISH USING ARTIFICIAL NEURAL NETWORK FOR ST1257B
Speed(rpm)
5000
3000
2000
Feed(inch/rev)
0.004
0.006
2.092706 2.353373
1.697164 1.902937
1.555699 1.712011
0.01
2.841483
2.376869
2.110624
TABLE 6.15
SURFACE FINISH USING ARTIFICIAL NEURAL NETWORK FOR ST1255G
Speed(rpm)
5000
3000
2000
Feed(inch/rev)
0.004
0.006
1.230692 1.430033
0.999933 1.165676
0.912598 1.048799
0.01
1.802249
1.54826
1.397568
After obtaining the output data from the optimum network these values were plotted in
comparison to experimental surface finish values for different speeds. Figures 6.2 to 6.8 show
this comparative plot for SCFL material at seven different speeds. Figures 6.9 to 6.11 show the
comparative plot for brad spur drill bit. Figures 6.12 to 6.14 show the comparative plot for
double margin drill bit. Figures 6.15 to 6.17 show the comparative plot for conventional drill bit.
Figures 6.18 to 6.20 show the comparative plot for ST1257B drill bit. Figures 6.21 to 6.23 show
the comparative plot for ST1255G drill bit. All the plots clearly indicate the increase in surface
finish value with increase in feed rate at a given cutting speed.
38
FEED VS SUR FINISH (5000 RPM)
.
85
S.FINISH (MIC-INCH)
95
75
A.N.N
Data
65
Exp
55
45
35
25
0
0.002
0.004
0.006
0.008
0.01
0.012
FEED (INCH /REV)
Figure 6.2. Neural Network Output (Feed Vs Surface Finish at 5,000 rpm)
FEED VS SUR FINISH (4500 RPM)
.
85
S.FINISH (MIC-INCH)
95
75
A.N.N
Data
65
Exp
55
45
35
25
0
0.002
0.004
0.006
0.008
0.01
0.012
FEED (INCH /REV)
Figure 6.3. Neural Network Output (Feed Vs Surface Finish at 4,500 rpm)
39
FEED VS SUR FINISH (4000 RPM)
.
85
S.FINISH (MIC-INCH)
95
75
A.N.N
Data
65
Exp
55
45
35
25
0
0.002
0.004
0.006
0.008
0.01
0.012
FEED (INCH /REV)
Figure 6.4. Neural Network Output (Feed Vs Surface Finish at 4,000 rpm)
FEED VS SUR FINISH (3500 RPM)
.
85
S.FINISH (MIC-INCH)
95
75
A.N.N
Data
65
Exp
55
45
35
25
0
0.002
0.004
0.006
0.008
0.01
0.012
FEED (INCH /REV)
Figure 6.5. Neural Network Output (Feed Vs Surface Finish at 3,500 rpm)
40
FEED VS SUR FINISH (3000 RPM)
.
85
S.FINISH (MIC-INCH)
95
75
A.N.N
Data
65
Exp
55
45
35
25
0
0.002
0.004
0.006
0.008
0.01
0.012
FEED (INCH /REV)
Figure 6.6. Neural Network Output (Feed Vs Surface Finish at 3,000 rpm)
FEED VS SUR FINISH (2500 RPM)
.
85
S.FINISH (MIC-INCH)
95
75
A.N.N
Data
65
Exp
c
55
45
35
25
0
0.002
0.004
0.006
0.008
0.01
0.012
FEED (INCH /REV)
Figure 6.7. Neural Network Output (Feed Vs Surface Finish at 2,500 rpm)
41
FEED VS SUR FINISH (2000 RPM)
.
85
S.FINISH (MIC-INCH)
95
75
A.N.N
Data
65
Exp
55
45
35
25
0
0.002
0.004
0.006
0.008
0.01
0.012
FEED (INCH /REV)
Figure 6.8. Neural Network Output (Feed Vs Surface Finish at 2,000 rpm)
Plots to show Surface finish trend of ANN Output for Brad spur
FEED VS SURFACE FINISH (@ 5000 RPM)
SUR FINISH (MIC-INCH) .
90
80
70
60
50
A.N.N Data
40
Exp
30
20
10
0
0
0.002
0.004
0.006
0.008
0.01
0.012
FEED RATE INCH/REV)
Figure 6.9. Neural Network Output (Feed Vs Surface Finish at 5,000 rpm)
42
FEED VS SURFACE FINISH (@ 3000 RPM)
SUR FINISH (MIC-INCH) .
70
60
50
40
A.N.N Data
30
Exp
20
10
0
0
0.002
0.004
0.006
0.008
0.01
0.012
FEED RATE INCH/REV)
Figure 6.10. Neural Network Output (Feed Vs Surface Finish at 3,000 rpm)
FEED VS SURFACE FINISH (@ 2000 RPM)
SUR FINISH (MIC-INCH) .
70
60
50
40
A.N.N Data
30
Exp
20
10
0
0
0.002
0.004
0.006
0.008
0.01
0.012
FEED RATE INCH/REV)
Figure 6.11. Neural Network Output (Feed Vs Surface Finish at 2,000 rpm)
43
Plots to show Surface finish trend of ANN Output for Double margin
SUR FINISH (MIC-INCH) .
FEED VS SURFACE FINISH (@ 5000 RPM)
120
110
100
90
80
70
60
50
40
30
20
10
0
A.N.N Data
Exp
0
0.002
0.004
0.006
0.008
0.01
0.012
FEED RATE (INCH/REV)
Figure 6.12. Neural Network Output (Feed Vs Surface Finish at 5,000 rpm)
SUR FINISH (MIC-INCH) .
FEED VS SURFACE FINISH (@ 3000 RPM)
110
100
90
80
70
60
50
40
30
20
10
0
A.N.N Data
Exp
0
0.002
0.004
0.006
0.008
0.01
0.012
FEED RATE (INCH/REV)
Figure 6.13. Neural Network Output (Feed Vs Surface Finish at 3,000 rpm)
44
SUR FINISH (MIC-INCH) .
FEED VS SURFACE FINISH (@ 2000 RPM)
100
90
80
70
60
50
40
30
20
10
0
A.N.N Data
Exp
0
0.002
0.004
0.006
0.008
0.01
0.012
FEED RATE (INCH/REV)
Figure 6.14. Neural Network Output (Feed Vs Surface Finish at 2,000 rpm)
Plots to show Surface finish trend of ANN Output for Conventional
FEED VS SURFACE FINISH (@ 5000 RPM)
SUR FINISH (MIC-INCH) .
60
50
40
A.N.N Data
30
Exp
20
10
0
0
0.002
0.004
0.006
0.008
0.01
0.012
FEED RATE (INCH/REV)
Figure 6.15. Neural Network Output (Feed Vs Surface Finish at 5,000 rpm)
45
FEED VS SURFACE FINISH (@ 3000 RPM)
SUR FINISH (MIC-INCH) .
45
40
35
30
25
A.N.N Data
20
Exp
15
10
5
0
0
0.002
0.004
0.006
0.008
0.01
0.012
FEED RATE (INCH/REV)
Figure 6.16. Neural Network Output (Feed Vs Surface Finish at 3,000 rpm)
FEED VS SURFACE FINISH (@ 2000 RPM)
SUR FINISH (MIC-INCH) .
45
40
35
30
25
A.N.N Data
20
Exp
15
10
5
0
0
0.002
0.004
0.006
0.008
0.01
0.012
FEED RATE (INCH/REV)
Figure 6.17. Neural Network Output (Feed Vs Surface Finish at 2,000 rpm)
46
Plots to show Surface finish trend of ANN Output for ST1257B
SUR FINISH (MIC-INCH) .
FEED VS SURFACE FINISH (@ 5000 RPM)
140
120
100
80
A.N.N Data
60
Exp
40
20
0
0
0.002
0.004
0.006
0.008
0.01
0.012
FEED RATE (INCH/REV)
Figure 6.18. Neural Network Output (Feed Vs Surface Finish at 5,000 rpm)
SUR FINISH (MIC-INCH) .
FEED VS SURFACE FINISH (@ 3000 RPM)
100
90
80
70
60
50
40
30
20
10
A.N.N Data
Exp
0
0.002
0.004
0.006
0.008
0.01
0.012
FEED RATE (INCH/REV)
Figure 6.19. Neural Network Output (Feed Vs Surface Finish at 3,000 rpm)
47
SUR FINISH (MIC-INCH) .
FEED VS SURFACE FINISH (@ 2000 RPM)
90
80
70
60
50
40
30
20
10
0
A.N.N Data
Exp
0
0.002
0.004
0.006
0.008
0.01
0.012
FEED RATE (INCH/REV)
Figure 6.20. Neural Network Output (Feed Vs Surface Finish at 2,000 rpm)
Plots to show Surface finish trend of ANN Output for ST1255G
FEED VS SURFACE FINISH (@ 5000 RPM)
SUR FINISH (MIC-INCH) .
80
70
60
50
A.N.N Data
40
Exp
30
20
10
0
0
0.002
0.004
0.006
0.008
0.01
0.012
FEED RATE (INCH/REV)
Figure 6.21. Neural Network Output (Feed Vs Surface Finish at 5,000 rpm)
48
FEED VS SURFACE FINISH (@ 3000 RPM)
SUR FINISH (MIC-INCH) .
70
60
50
40
A.N.N Data
30
Exp
20
10
0
0
0.002
0.004
0.006
0.008
0.01
0.012
FEED RATE (INCH/REV)
Figure 6.22. Neural Network Output (Feed Vs Surface Finish at 3,000 Rpm)
FEED VS SURFACE FINISH (@ 2000 RPM)
SUR FINISH (MIC-INCH) .
60
50
40
A.N.N Data
30
Exp
20
10
0
0
0.002
0.004
0.006
0.008
0.01
0.012
FEED RATE (INCH/REV)
Figure 6.23. Neural Network Output (Feed Vs Surface Finish at 2,000 rpm)
6.2 Design of Experiments
The Factorial Design
Factorial designs are widely used in experiments involving several factors where it is
49
necessary to study the joint effect of the factors on a response. However, there are several
special cases of the general factorial design that are important because they are widely used in
research work, and also because they form the basis of other designs of considerable practical
value.
The most important of these special cases is that of k factors, which are at only two
levels. These levels may be quantitative, such as two values of temperature, pressure, or time.
They may be qualitative, such as two machines, two operators, the “high” and “low” levels of a
factor, or perhaps the presence and absence of a factor. A complete replicate of such a design
requires 2 x 2 x … x 2 = 2 k observations and is called a 2 k factorial design.
Here we assume that (1) the factors are fixed, (2) the designs are completely randomized,
and (3) the usual normality assumptions are satisfied.
The 2k design is particularly useful in the early stages of experimental work, where there
are likely to be many factors to be investigated. It provides the smallest number of runs with
which k factors can be studied in a complete factorial design. Because there are only two levels
for each factor, we must assume that the response is approximately linear over the range of the
factor levels chosen.
Before conducting the actual experiments the experiments were designed using Stat-ease
Software. Factorial method was employed to design the experiments with 2 input variables
(speed, feed-rate) and one output (surface finish value).Machining was carried out based on the
run order and randomization obtained from the software.
ANOVA was done only to supplement the results obtained from Neural networks and not to
actually observe the experimental data in detail. The DOE input is shown in Table 6.16. The
ANOVA output results for SCFL material are tabulated in Table 6.17.
50
TABLE 6.16
DESIGN OF EXPERIMENT INPUT DATA FOR SCFL MATERIAL
S. No
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
Run order
23
41
7
95
40
76
46
109
34
9
118
44
67
94
8
39
12
96
97
119
122
1
58
33
75
16
42
126
86
74
2
71
51
116
35
32
30
19
89
108
29
25
43
Speed rpm
5000
5000
5000
4500
4500
4500
4000
4000
4000
3500
3500
3500
3000
3000
3000
2500
2500
2500
2000
2000
2000
5000
5000
5000
4500
4500
4500
4000
4000
4000
3500
3500
3500
3000
3000
3000
2500
2500
2500
2000
2000
2000
5000
51
Feed ipr
.001
.001
.001
.001
.001
.001
.001
.001
.001
.001
.001
.001
.001
.001
.001
.001
.001
.001
.001
.001
.001
.002
.002
.002
.002
.002
.002
.002
.002
.002
.002
.002
.002
.002
.002
.002
.002
.002
.002
.002
.002
.002
.004
Surface Finish value µm
1.1
0.96
1.04
0.98
0.7
1.06
1.03
0.87
1.12
0.94
1.02
0.86
0.76
0.89
1.01
1.1
0.95
0.87
0.96
1.05
0.92
1.28
1.21
1.16
1.22
1.32
1.29
1.19
1.3
1.27
1.3
1.18
1.41
1.22
1.37
1.43
1.18
1.37
1.26
1.32
1.19
1.27
1.32
TABLE 6.16 (cont)
DESIGN OF EXPERIMENT INPUT DATA FOR SCFL MATERIAL
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
100
104
15
11
10
3
70
65
52
28
5
103
83
117
115
92
112
107
110
27
85
84
57
21
87
22
101
99
60
49
106
47
20
98
26
6
55
73
14
68
91
5000
5000
4500
4500
4500
4000
4000
4000
3500
3500
3500
3000
3000
3000
2500
2500
2500
2000
2000
2000
5000
5000
5000
4500
4500
4500
4000
4000
4000
3500
3500
3500
3000
3000
3000
2500
2500
2500
2000
2000
2000
.004
.004
.004
.004
.004
.004
.004
.004
.004
.004
.004
.004
.004
.004
.004
.004
.004
.004
.004
.004
.006
.006
.006
.006
.006
.006
.006
.006
.006
.006
.006
.006
.006
.006
.006
.006
.006
.006
.006
.006
.006
52
1.29
1.38
1.32
1.19
1.45
1.24
1.29
1.36
1.13
1.24
1.19
1.22
1.15
1.2
1.21
1.14
1.29
1.23
1.29
1.19
1.46
1.56
1.6
1.49
1.57
1.51
1.42
1.5
1.58
1.37
1.43
1.55
1.36
1.45
1.51
1.39
1.45
1.31
1.44
1.39
1.49
TABLE 6.16 (cont)
DESIGN OF EXPERIMENT INPUT DATA FOR SCFL MATERIAL
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
120
45
64
54
38
81
4
69
90
80
61
77
18
66
125
82
102
124
56
13
72
121
114
111
24
88
31
123
53
62
93
59
79
17
37
50
63
113
48
105
36
78
5000
5000
5000
4500
4500
4500
4000
4000
4000
3500
3500
3500
3000
3000
3000
2500
2500
2500
2000
2000
2000
5000
5000
5000
4500
4500
4500
4000
4000
4000
3500
3500
3500
3000
3000
3000
2500
2500
2500
2000
2000
2000
.008
.008
.008
.008
.008
.008
.008
.008
.008
.008
.008
.008
.008
.008
.008
.008
.008
.008
.008
.008
.008
.01
.01
.01
.01
.01
.01
.01
.01
.01
.01
.01
.01
.01
.01
.01
.01
.01
.01
.01
.01
.01
53
1.59
1.61
1.7
1.64
1.69
1.72
1.61
1.75
1.59
1.67
1.55
1.72
1.54
1.66
1.73
1.59
1.79
1.61
1.65
1.74
1.56
1.72
1.8
1.68
1.59
1.71
1.85
1.77
1.89
2.01
1.78
1.62
1.59
1.68
1.76
1.9
1.64
1.73
1.59
1.71
1.82
1.63
TABLE 6.17
TABULATION OF SURFACE FINISH OUTPUT USING DOE FOR SCFL MATERIAL
Speed(rpm)
5000
4500
4000
3500
3000
2500
2000
0.001
1.033333
0.913333
1.006667
0.94
0.886667
0.973333
0.976667
0.002
1.216667
1.276667
1.253333
1.296667
1.34
1.27
1.26
Feed (inch/rev)
0.004
0.006
1.33
1.54
1.32
1.523333
1.296667 1.5
1.186667 1.45
1.19
1.44
1.213333 1.383333
1.236667 1.34
0.008
1.633333
1.683333
1.65
1.646667
1.643333
1.663333
1.65
0.01
1.73333
1.716667
1.716667
1.663333
1.78
1.653333
1.72
The response plot is as shown in Figure 6.24. Also the governing equation obtained by
ANOVA is in form of equation (6.3).
DESIGN-EXPERT Plot
S.FINISH .Ra
X = A: SPEED
Y = B: FEED
1.761
S.Finish (micro-mtr)
1.575
1.389
1.203
1.017
0.010
5000
0.007
4250
0.005
3500
B: Feed (inch/rev)0.002
2750
0.000
A: Speed (RPM)
2000
Figure 6.24. Response Surface for DOE predicted Output for SCFL
54
Governing ANOVA equation for SFCL data is as follows:
S.F=1.44+0.029*A+0.34*B+9.048*e-003*A2-0.060*B2+4.033e-003*A*B
(6.3)
The DOE input data for brad spur drill bit is shown in Table 6.18.
TABLE 6.18
DESIGN OF EXPERIMENT INPUT DATA FOR BRAD SPUR
Run
Order
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
Speed
rpm
5000
3000
3000
2000
2000
2000
5000
3000
5000
5000
3000
2000
3000
2000
3000
3000
3000
5000
2000
2000
5000
5000
3000
5000
2000
2000
5000
Feed
inch/rev
0.01
0.004
0.004
0.004
0.004
0.004
0.01
0.006
0.006
0.004
0.006
0.01
0.01
0.006
0.004
0.01
0.006
0.006
0.01
0.006
0.004
0.01
0.01
0.004
0.01
0.006
0.006
S.Finish
µm
2.01
1.15
1.17
1.06
1.05
1.07
2.03
1.35
1.85
1.35
1.33
1.45
1.54
1.25
1.15
1.56
1.37
1.85
1.49
1.23
1.35
2.05
1.55
1.37
1.48
1.26
1.89
The output data from ANOVA for brad spur drill bit is tabulated in Table 6.19. The
response plot for brad spur is as shown in Figure 6.25. Also the governing equation obtained by
ANOVA is in form of equation (6.4).
55
TABLE 6.19
TABULATION OF SURFACE FINISH OUTPUT USING DOE FOR BRAD SPUR
Feed rate
inch/rev
0.004
0.006
0.01
Speed rpm
3000
2000
5000
1.356667
1.863333
2.03
1.156667
1.35
1.55
1.06
1.246667
1.473333
DESIGN-EXPERT Plot
surf ace roughness
X = A: speed
Y = B: Feed
2.051
S.Finish (mic-mtr)
1.799
1.547
1.294
1.042
0.010
5000.00
0.009
4250.00
0.007
3500.00
B: Feed (inch/rev)0.006
2750.00
A: Speed (RPM)
0.004
2000.00
Figure 6.25. Response Surface for DOE predicted Output for Brad spur
Governing ANOVA equation for brad spur drill data is as follows:
S.F =1.56+0.25*A+0.25*B+0.080*A2-0.15*B2+0.057*A*B
(6.4)
The DOE input data for double margin drill bit is shown in Table 6.20. The ANOVA
output data is tabulated in Table 6.21. The response plot for double margin is as shown in Figure
6.26. Also the governing equation obtained by ANOVA is in form of equation (6.5).
56
TABLE 6.20
DESIGN OF EXPERIMENT INPUT DATA FOR DOUBLE MARGIN
Run
Order
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
Speed
rpm
5000
3000
3000
2000
2000
2000
5000
3000
5000
5000
3000
2000
3000
2000
3000
3000
3000
5000
2000
2000
5000
5000
3000
5000
2000
2000
5000
Feed
inch/rev
0.01
0.004
0.004
0.004
0.004
0.004
0.01
0.006
0.006
0.004
0.006
0.01
0.01
0.006
0.004
0.01
0.006
0.006
0.01
0.006
0.004
0.01
0.01
0.004
0.01
0.006
0.006
S.Finish
µm
2.73
2
2.01
1.93
1.95
1.97
2.7
2.27
2.55
2.23
2.28
2.33
2.53
2.13
2.03
2.5
2.25
2.55
2.35
2.15
2.25
2.75
2.52
2.25
2.33
2.14
2.52
TABLE 6.21
TABULATION OF SURFACE FINISH OUTPUT USING DOE FOR DOUBLE MARGIN
Feed rate
inch/rev
0.004
0.006
0.01
Speed rpm
3000
2000
5000
2.243333
2.666667
2.6
57
2.013333
2.266667
2.516667
1.95
2.14
2.336667
DESIGN-EXPERT Plot
surf ace roughness
X = A: speed
Y = B: Feed
2.746
S.Finish (mic-mtr)
2.540
2.334
2.127
1.921
0.010
5000.00
0.009
4250.00
0.007
3500.00
B: Feed (inch/rev)0.006
2750.00
A: Speed (RPM)
0.004
2000.00
Figure 6.26. Response Surface for DOE predicted Output for Double margin
Governing ANOVA equation for double margin drill data is as follows:
S.F =2.42+0.18*A+0.23*B-0.11*B2+0.016*A*B
(6.5)
The DOE input data for conventional drill bit is shown in Table 6.22. The ANOVA
output data is tabulated in Table 6.23. The response plot for conventional is as shown in Figure
6.27. Also the governing equation obtained by ANOVA is in form of equation (6.6).
The DOE input data for ST1257B drill bit is shown in Table 6.24. The ANOVA output
data is tabulated in Table 6.25. The response plot for ST1257B is as shown in Figure 6.28. Also
the governing equation obtained by ANOVA is in form of equation (6.7).
The DOE input data for ST1255G bit is shown in Table 6.26. The ANOVA output data is
tabulated in Table 6.27. The response plot for ST1255G is as shown in Figure 6.29. Also the
governing equation obtained by ANOVA is in form of equation (6.8).
58
TABLE 6.22
DESIGN OF EXPERIMENT INPUT DATA FOR CONVENTIONAL
Run
Order
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
Speed
rpm
5000
3000
3000
2000
2000
2000
5000
3000
5000
5000
3000
2000
3000
2000
3000
3000
3000
5000
2000
2000
5000
5000
3000
5000
2000
2000
5000
Feed
inch/rev
0.01
0.004
0.004
0.004
0.004
0.004
0.01
0.006
0.006
0.004
0.006
0.01
0.01
0.006
0.004
0.01
0.006
0.006
0.01
0.006
0.004
0.01
0.01
0.004
0.01
0.006
0.006
S.Finish
µm
1.2
0.59
0.6
0.56
0.57
0.58
1.22
0.75
0.95
0.7
0.8
0.91
1
0.78
0.62
0.99
0.78
0.9
0.95
0.81
0.72
1.23
0.99
0.73
0.96
0.8
0.92
TABLE 6.23
TABULATION OF SURFACE FINISH OUTPUT USING DOE FOR CONVENTIONAL
Feed rate
inch/rev
0.004
0.006
0.01
Speed rpm
3000
2000
5000
0.716667
0.923333
1.216667
59
0.603333
0.776667
0.993333
0.57
0.796667
0.94
DESIGN-EXPERT Plot
surf ace roughness
X = A: speed
Y = B: Feed
1.210
S.Finish (mic-mtr)
1.054
0.898
0.741
0.585
0.010
5000.00
0.009
4250.00
0.007
3500.00
B: Feed (inch/rev)0.006
2750.00
A: Speed RPM
0.004
2000.00
Figure 6.27. Response Surface for DOE predicted Output for Conventional
Governing ANOVA equation for conventional drill data is as follows:
S.F =0.89+0.096*A+0.21*B+0.044*A2-0.070*B2+0.037*A*B
DESIGN-EXPERT Plot
surf ace roughness
X = A: speed
Y = B: Feed
2.968
S.Finish (mic-mtr)
2.643
2.317
1.992
1.667
0.010
5000.00
0.009
4250.00
0.007
3500.00
B: Feed (inch/rev)0.006
2750.00
A: Speed (RPM)
0.004
2000.00
Figure 6.28. Response Surface for DOE predicted Output for ST1257B
60
(6.6)
TABLE 6.24
DESIGN OF EXPERIMENT INPUT DATA FOR ST1257B
Run
Order
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
Speed
rpm
5000
3000
3000
2000
2000
2000
5000
3000
5000
5000
3000
2000
3000
2000
3000
3000
3000
5000
2000
2000
5000
5000
3000
5000
2000
2000
5000
Feed
inch/rev
0.01
0.004
0.004
0.004
0.004
0.004
0.01
0.006
0.006
0.004
0.006
0.01
0.01
0.006
0.004
0.01
0.006
0.006
0.01
0.006
0.004
0.01
0.01
0.004
0.01
0.006
0.006
S.Finish
µm
2.95
1.72
1.72
1.68
1.69
1.65
2.96
2.03
2.39
1.99
2.01
2.05
2.35
1.89
1.75
2.36
1.99
2.35
2.01
1.85
1.99
2.98
2.35
1.98
2.02
1.89
2.37
TABLE 6.25
TABULATION OF SURFACE FINISH OUTPUT USING DOE FOR ST1257B
Feed rate
inch/rev
0.004
0.006
0.01
Speed rpm
3000
2000
5000
1.986667
2.37
2.963333
61
1.73
2.01
2.353333
1.673333
1.876667
2.026667
Governing ANOVA equation for ST1257B drill data is as follows:
S.F =2.22+0.31*A+0.34*B+0.024*A2-0.081*B2+0.15*A*B
TABLE 6.26
DESIGN OF EXPERIMENT INPUT DATA FOR ST1255G
Run
Order
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
Speed
rpm
5000
3000
3000
2000
2000
2000
5000
3000
5000
5000
3000
2000
3000
2000
3000
3000
3000
5000
2000
2000
5000
5000
3000
5000
2000
2000
5000
Feed
inch/rev
0.01
0.004
0.004
0.004
0.004
0.004
0.01
0.006
0.006
0.004
0.006
0.01
0.01
0.006
0.004
0.01
0.006
0.006
0.01
0.006
0.004
0.01
0.01
0.004
0.01
0.006
0.006
S.Finish
µm
1.84
1.05
1.02
0.95
0.98
0.96
1.88
1.28
1.65
1.15
1.25
1.35
1.55
1.15
0.98
1.55
1.25
1.64
1.36
1.12
1.19
1.85
1.54
1.2
1.35
1.15
1.65
TABLE 6.27
TABULATION OF SURFACE FINISH OUTPUT USING DOE FOR ST1255G
Feed rate
inch/rev
0.004
0.006
0.01
Speed rpm
3000
2000
5000
1.18
1.646667
1.856667
62
1.016667
1.26
1.546667
0.963333
1.14
1.353333
(6.7)
DESIGN-EXPERT Plot
surf ace roughness
X = A: speed
Y = B: Feed
1.889
S.Finish (mic-mtr)
1.648
1.406
1.164
0.922
0.010
5000.00
0.009
4250.00
0.007
3500.00
B: Feed (inch/rev)0.006
2750.00
A: Speed (RPM)
0.004
2000.00
Figure 6.29. Response Surface for DOE predicted Output for ST1255G
Governing ANOVA equation for ST1255G drill data is as follows:
S.F =1.46+0.21*A+0.27*B+0.016*A2-0.13*B2+0.058*A*B
(6.8)
6.3 Data Comparison
Based on the surface finish data obtained from five different drill bits for BMS 8276 form 3 material, the drill bit performance was compared. For each individual speed and feed
rate the experimental surface finish values for different drill bits were plotted as shown in
Figures 6.30 to 6.38. The drill bits based on their degree of performance are arranged as follows:
(1). Conventional (2). ST1255G (3). Brad spur (4). ST1257B (5). Double margin. This signified
that for any given speed and feed rate, the conventional drill bit showed the best performance
and the double margin drill bit had the worst. Also a three dimensional plot explaining the same
phenomenon is shown in Figure 6.39. This comparison helped to understand the behavior of each
drill bit for different speed and feed rate conditions for the BMS 8-276 form 3 material. The
surface finish value comparison for different drill bits is done in chapter seven.
63
SURFACE FINISH @ 5000 RPM, 0.004 INCH/REV
100
SUR FINISH (MIC-MICH).
90
80
70
60
5000 RPM,
0.004 inch/rev
50
40
30
20
10
0
BS
DM
CD
ST1255G ST1257B
DRILL BITS
Figure 6.30. Surface finish values at 5,000 rpm and 0.004 inch/rev
SURFACE FINISH @ 5000 RPM, 0.006 INCH/REV
SUR FINISH (MIC-MICH).
120
100
80
5000 RPM,
0.006 inch/rev
60
40
20
0
BS
DM
CD
ST1255G ST1257B
DRILL BITS
Figure 6.31. Surface finish values at 5,000 rpm and 0.006 inch/rev
64
SURFACE FINISH @ 5000 RPM, 0.01 INCH/REV
SUR FINISH (MIC-MICH).
140
120
100
80
5000 RPM,
0.01 inch/rev
60
40
20
0
BS
DM
CD
ST1255G ST1257B
DRILL BITS
Figure 6.32. Surface finish values at 5,000 rpm and 0.01 inch/rev
SURFACE FINISH @ 3000 RPM, 0.004 INCH/REV
90
SUR FINISH (MIC-MICH).
80
70
60
50
3000 RPM,
0.004 inch/rev
40
30
20
10
0
BS
DM
CD
ST1255G ST1257B
DRILL BITS
Figure 6.33. Surface finish values at 3,000 rpm and 0.004 inch/rev
65
SURFACE FINISH @ 3000 RPM, 0.006 INCH/REV
100
SUR FINISH (MIC-MICH).
90
80
70
60
3000 RPM,
0.006 inch/rev
50
40
30
20
10
0
BS
DM
CD
ST1255G ST1257B
DRILL BITS
Figure 6.34. Surface finish values at 3,000 rpm and 0.006 inch/rev
SURFACE FINISH @ 3000 RPM, 0.01 INCH/REV
SUR FINISH (MIC-MICH).
120
100
80
3000 RPM,
0.01 inch/rev
60
40
20
0
BS
DM
CD
ST1255G ST1257B
DRILL BITS
Figure 6.35. Surface finish values at 3,000 rpm and 0.01 inch/rev
66
SURFACE FINISH @ 2000 RPM, 0.004 INCH/REV
90
SUR FINISH (MIC-MICH).
80
70
60
50
40
2000 RPM,
0.004 inch/rev
30
20
10
0
BS
DM
CD
ST1255G ST1257B
DRILL BITS
Figure 6.36. Surface finish values at 2,000 rpm and 0.004 inch/rev
SURFACE FINISH @ 2000 RPM, 0.006 INCH/REV
90
SUR FINISH (MIC-MICH).
80
70
60
50
40
2000 RPM,
0.006 inch/rev
30
20
10
0
BS
DM
CD
ST1255G ST1257B
DRILL BITS
Figure 6.37. Surface finish values at 2,000 rpm and 0.006 inch/rev
67
SURFACE FINISH @ 2000 RPM, 0.01 INCH/REV
100
SUR FINISH (MIC-MICH).
90
80
70
60
50
40
2000 RPM,
0.01 inch/rev
30
20
10
0
BS
DM
CD
ST1255G ST1257B
DRILL BITS
Figure 6.38. Surface finish values at 2,000 rpm and 0.01 inch/rev
Comparison of Drill bit performance
120
80
60
40
20
0
r
1 ip
0.0
M,
RP
ipr
00
06
20
0.0
M,
RP
ipr
00
04
20
0.0
M,
RP
00
pr
20
1i
0.0
M,
RP
00
ipr
30
06
0.0
M,
RP
00
ipr
30
04
0.0
M,
RP
00
pr
30
1i
0.0
M,
RP
00
ipr
50
06
0.0
M,
RP
00
ipr
50
04
0.0
M,
RP
00
50
DRILL
BITS
BS
DM
CD
ST1255G
ST1257B
Figure 6.39. Comparison of Drill bits
68
SUR FINISH (MIC-INCH) .
100
CHAPTER 7
RESULTS AND DISCUSSION
The lowest and highest experimental surface finish values for SCFL material is tabulated
in Table 7.1. Similarly the neural network data and DOE predicted data are in Table 7.2 and 7.3
respectively.
TABLE 7.1
EXPERIMENTAL DATA FOR SCFL MATERIAL
Speed (rpm)
Feed (ipr)
Surface Finish (µm)
Lowest
4500
0.001
0.7
Highest
4000
0.01
2.01
TABLE 7.2
NEURAL NETWORK DATA FOR SCFL MATERIAL
Speed (rpm)
Feed (ipr)
Surface Finish (µm)
Lowest
4500
0.001
0.95
Highest
4000
0.01
1.82
TABLE 7.3
DOE PREDICTED DATA FOR SCFL MATERIAL
Speed (rpm)
Feed (ipr)
Lowest
4500
0.001
0.89
Highest
4000
0.01
1.78
69
Surface Finish (µm)
The lowest and highest experimental surface finish values for brad spur drill bit used in
drilling of BMS 8-276 form 3 material is tabulated in Table 7.4. Similarly the neural network
data and DOE predicted data are in Table 7.5 and 7.6 respectively.
TABLE 7.4
EXPERIMENTAL DATA FOR BRAD SPUR
Speed (rpm)
Feed (ipr)
Surface Finish (µm)
Lowest
5000
0.004
1.05
Highest
2000
0.01
2.05
TABLE 7.5
NEURAL NETWORK DATA FOR BRAD SPUR
Speed (rpm)
Feed (ipr)
Surface Finish (µm)
Lowest
5000
0.004
1.47
Highest
2000
0.01
1.94
TABLE 7.6
DOE PREDICTED DATA FOR BRAD SPUR
Speed (rpm)
Feed (ipr)
Surface Finish (µm)
Lowest
5000
0.004
1.06
Highest
2000
0.01
2.03
The lowest and highest experimental surface finish values for double margin drill bit used
in drilling of BMS 8-276 form 3 material is tabulated in Table 7.7. Similarly the neural network
data and DOE predicted data are in Table 7.8 and 7.9 respectively.
70
TABLE 7.7
EXPERIMENTAL DATA FOR DOUBLE MARGIN
Speed (rpm)
Feed (ipr)
Surface Finish (µm)
Lowest
5000
0.001
1.93
Highest
2000
0.006
2.75
TABLE 7.8
NEURAL NETWORK DATA FOR DOUBLE MARGIN
Speed (rpm)
Feed (ipr)
Surface Finish (µm)
Lowest
5000
0.001
1.93
Highest
2000
0.004
2.79
TABLE 7.9
DOE PREDICTED DATA FOR DOUBLE MARGIN
Speed (rpm)
Feed (ipr)
Surface Finish (µm)
Lowest
5000
0.001
1.95
Highest
2000
0.06
2.67
The lowest and highest experimental surface finish values for conventional drill bit used
in drilling of BMS 8-276 form 3 material is tabulated in Table 7.10. Similarly the neural network
data and DOE predicted data are in Table 7.11 and 7.12 respectively.
The lowest and highest experimental surface finish values for ST1257B drill bit used in
drilling of BMS 8-276 form 3 material is tabulated in Table 7.13. Similarly the neural network
data and DOE predicted data are in Table 7.14 and 7.15 respectively.
71
TABLE 7.10
EXPERIMENTAL DATA FOR CONVENTIONAL
Speed (rpm)
Feed (ipr)
Surface Finish (µm)
Lowest
5000
0.004
0.56
Highest
2000
0.01
1.23
TABLE 7.11
NEURAL NETWORK DATA FOR CONVENTIONAL
Speed (rpm)
Feed (ipr)
Surface Finish (µm)
Lowest
5000
0.004
0.54
Highest
2000
0.01
1.18
TABLE 7.12
DOE PREDICTED DATA FOR CONVENTIONAL
Speed (rpm)
Feed (ipr)
Surface Finish (µm)
Lowest
5000
0.004
0.57
Highest
2000
0.01
1.23
TABLE 7.13
EXPERIMENTAL DATA FOR ST1257B
Speed (rpm)
Feed (ipr)
Surface Finish (µm)
Lowest
5000
0.004
1.65
Highest
2000
0.01
2.98
72
TABLE 7.14
NEURAL NETWORK DATA FOR ST1257B
Speed (rpm)
Feed (ipr)
Surface Finish (µm)
Lowest
5000
0.004
1.56
Highest
2000
0.01
2.84
TABLE 7.15
DOE PREDICTED DATA FOR ST1257B
Speed (rpm)
Feed (ipr)
Surface Finish (µm)
Lowest
5000
0.004
1.67
Highest
2000
0.01
2.96
TABLE 7.16
EXPERIMENTAL DATA FOR ST1255G
Speed (rpm)
Feed (ipr)
Surface Finish (µm)
Lowest
5000
0.004
0.96
Highest
2000
0.01
1.88
TABLE 7.17
NEURAL NETWORK DATA FOR ST1255G
Speed (rpm)
Feed (ipr)
Surface Finish (µm)
Lowest
5000
0.004
0.91
Highest
2000
0.01
1.80
73
TABLE 7.18
DOE PREDICTED DATA FOR ST1255G
Speed (rpm)
Feed (ipr)
Surface Finish (µm)
Lowest
5000
0.004
0.96
Highest
2000
0.01
1.86
The lowest and highest experimental surface finish values for ST1255G drill bit used in
drilling of BMS 8-276 form 3 material is tabulated in Table 7.16. Similarly the neural network
data and DOE predicted data are in Table 7.17 and 7.18 respectively.
It is evident by observing the experimental data that surface finish value increases with
feed rate at given cutting speed.
The behavior of surface finish with respect to different cutting speed values could not be
established.
The percentage error with which the optimum networks were able to predict surface
finish values is as follows,
Error %
A. SCFL material
4.1%
B. BMS 8-276 form-3
1. Brad spur drill bit
5.05 %
2. Double margin drill bit
3.45 %
3. Conventional drill bit
2.59 %
4. ST1257B
3.86 %
5. ST1255G
4.75 %
74
The Analysis of Variance was done to verify the behavior of the data. It is in agreement
with the Neural Network output.
These results clearly state the relationship of Surface finish with feed rate at given cutting
speed. Use of high speed (around 5,000 rpm) with a adequate low feed rate (0.004 ipr) could
result in a specimen with reasonable surface finish with efficient drilling economics.
The pictorial representation of the surface of drilled specimens gives a better insight into
the results obtained. The magnified pictures of the drilled surface are shown in Appendix C.
From observation of these pictures it can be seen that surfaces have uniform surface irregularities
at low feed-rates while they show some unusual deformity at higher feed rates. These results
provide a precise platform for further experimental investigation.
75
CHAPTER 8
CONCLUSION
•
Analysis of the experimental data clearly signifies that surface finish deteriorates with
increase in feed rate at a given cutting speed.
•
A relationship of surface finish with respect to cutting speed could not be established.
•
Other factors such as drill geometry, work-piece properties and machining conditions
definitely have influence on the hole quality, but no experimental investigation was done.
•
The results obtained from Neural network analysis are in good agreement with the
experimental results.
•
The ANOVA on experimental data supports the fact that feed rate is the significant factor
in the experiment.
•
The network provides a platform for future experiments to be conducted.
76
CHAPTER 9
LIMITATIONS
•
Input factors considered that affected the output (Surface finish value) were only speed
and feed.
•
Other governing factors such as drill geometry, material properties and machine
inaccuracies were not considered.
•
Unavailability of drill specifications and composite material properties narrowed the
overall analysis.
77
CHAPTER 10
FUTURE WORK
•
Study other factors that affect surface finish other than feed-rate and speed.
•
There are other factors that determine the overall hole quality, such as roundness, actual
diameter which also can be investigated.
•
Different learning and activation functions can be tested for the same data to investigate
the network behavior.
•
Relationship of hole quality with respect to cutting force and torque can be investigated.
•
Experiments with different drill bits on the same material can be done and comparison
with the current experiments can be carried out.
78
LIST OF REFERENCES
79
LIST OF REFERENCES
[1]
“Metal Cutting Tool Handbook,” 1954, published by, Metal Cutting Tool Institute, pp345, 3rd Edition.
[2]
Mel, Schwartz., 1992, “Composite Material Handbook,” 2nd Edition.
[3]
Brian, Lambert K., 1979, “Prediction of Force, Torque, and Burr Length in Drilling
Titanium-Composite Materials,” SME Technical Paper, 3p.
[4]
Chandrashekaran, V., Kapoor, S.G., Devor, R.E., November 1995, “A Mechanistic
Approach to Predicting the Cutting Forces in Drilling: With Application to FiberReinforced Composite Materials,” Journal of Engineering for Industry, 117, pp. 559-570.
[5]
Tosun, Gul., Muratoglu Mehtap., August 2004, “The drilling of AL/SICP Metal-Matrix
Composites, Part II: Work piece Surface Integrity,” Journal of Composites Science and
Technology, 64, pp. 1413-1418.
[6]
Enemuoh, Ugo E., El, Gizawy A., Okafor, Chukwujekwu A., 1999, “Neural Network
Based Sensor Fusion for On-line Prediction of Delamination and Surface Roughness in
Drilling AS4/PEER Composites,” SME Technical Paper, pp. 187.1 – 187.6.
[7]
Davim, J.P., Monteiro, Baptista A., 2001, “Cutting Force, Tool Wear and Surface Finish
in Drilling Metal Matrix Composites,” Proceedings of the Institution of Mechanical
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81
APPENDICES
82
APPENDIX A
OPTIMUM ARTIFICIAL NEURAL NETWORKS
Figure A.1. Neural network showing two hidden layers with five nodes each
Figure A.2. Neural network showing one hidden layer with four nodes each
83
APPENDIX B
OUTPUT DATA FROM DOE AND NEURAL NETWORK ANALYSIS
TABLE B.1
OUTPUT DATA FROM DOE AND NEURAL NETWORK ANALYSIS FOR SFCL
Speed (rpm)
5000
5000
5000
5000
5000
5000
4500
4500
4500
4500
4500
4500
4000
4000
4000
4000
4000
4000
3500
3500
3500
3500
3500
3500
3000
3000
3000
3000
3000
3000
2500
2500
2500
2500
2500
2500
2000
Feed (ipr)
0.001
0.002
0.004
0.006
0.008
0.01
0.001
0.002
0.004
0.006
0.008
0.01
0.001
0.002
0.004
0.006
0.008
0.01
0.001
0.002
0.004
0.006
0.008
0.01
0.001
0.002
0.004
0.006
0.008
0.01
0.001
0.002
0.004
0.006
0.008
0.01
0.001
DOE Sur.Finish (µm)
1.033333
1.216667
1.33
1.54
1.633333
1.73333
0.913333
1.276667
1.32
1.523333
1.683333
1.716667
1.006667
1.253333
1.296667
1.5
1.65
1.716667
0.94
1.296667
1.186667
1.45
1.646667
1.663333
0.886667
1.34
1.19
1.44
1.643333
1.78
0.973333
1.27
1.213333
1.383333
1.663333
1.653333
0.976667
84
ANN Sur.Finish (µm)
1.030617
1.116811
1.290615
1.474671
1.649249
1.820094
1.016725
1.101726
1.273175
1.456223
1.63311
1.804131
1.003551
1.086215
1.256279
1.438014
1.616943
1.787775
0.991108
1.070471
1.240016
1.420166
1.600985
1.771111
0.979393
1.054718
1.224458
1.402793
1.585451
1.754237
0.968393
1.03919
1.209658
1.385999
1.570503
1.737263
0.958078
TABLE B.1 (cont)
OUTPUT DATA FROM DOE AND NEURAL NETWORK ANALYSIS FOR SFCL
2000
2000
2000
2000
2000
0.002
0.004
0.006
0.008
0.01
1.26
1.236667
1.34
1.65
1.72
1.024099
1.195652
1.369872
1.556249
1.720307
TABLE B.2
OUTPUT DATA FROM NEURAL NETWORK FOR SCFL WITH LEAST RMS ERROR
VALUE (OUTPUT FOR 1 HIDDEN LAYER -4 NODES. RMS ERROR= 0.169401)
INPUT
1.28
1.21
1.16
1.59
1.61
1.7
1.22
1.32
1.29
1.64
1.69
1.72
1.19
1.3
1.27
1.61
1.75
1.59
1.3
1.18
1.41
OUTPUT
1.116811
1.116811
1.116811
1.649249
1.649249
1.649249
1.101726
1.101726
1.101726
1.63311
1.63311
1.63311
1.086215
1.086215
1.086215
1.616943
1.616943
1.616943
1.070471
1.070471
1.070471
ERROR
0.026631
0.008684
0.001865
0.00351
0.00154
0.002576
0.013989
0.047644
0.035447
4.75E-05
0.003236
0.00755
0.010771
0.045704
0.033777
4.82E-05
0.017704
0.000726
0.052684
0.011997
0.11528
INPUT
1.67
1.55
1.72
1.22
1.37
1.43
1.54
1.66
1.73
1.18
1.37
1.26
1.59
1.79
1.61
1.32
1.19
1.27
1.65
1.74
1.56
OUTPUT
1.600985
1.600985
1.600985
1.054718
1.054718
1.054718
1.585451
1.585451
1.585451
1.03919
1.03919
1.03919
1.570503
1.570503
1.570503
1.024099
1.024099
1.024099
1.556249
1.556249
1.556249
ERROR
0.004763
0.002599
0.014165
0.027318
0.099403
0.140837
0.002066
0.005558
0.020894
0.019827
0.109435
0.048757
0.00038
0.048179
0.00156
0.087557
0.027523
0.060467
0.008789
0.033764
1.41E-05
TABLE B.3
OUTPUT DATA FROM NEURAL NETWORK FOR BRAD SPUR WITH LEAST RMS
ERROR VALUE (OUTPUT FOR 2 HIDDEN LAYERS -5 NODES. RMS ERROR= 0.169615)
INPUT
1.89
1.85
1.85
1.33
OUTPUT
1.598431
1.598431
1.598431
1.280777
85
ERROR
0.085012
0.063287
0.063287
0.002423
TABLE B.3 (cont)
OUTPUT DATA FROM NEURAL NETWORK FOR BRAD SPUR WITH LEAST RMS
ERROR VALUE (OUTPUT FOR 2 HIDDEN LAYERS -5 NODES. RMS ERROR= 0.169615)
1.35
1.37
1.23
1.26
1.25
1.280777
1.280777
1.14388
1.14388
1.14388
0.004792
0.007961
0.007417
0.013484
0.011261
TABLE B.4
OUTPUT DATA FROM NEURAL NETWORK FOR DOUBLE MARGIN WITH LEAST RMS
ERROR VALUE (OUTPUT FOR 2 HIDDEN LAYERS -5 NODES. RMS ERROR= 0.102468)
INPUT
2.55
2.55
2.52
2.27
2.28
2.25
2.13
2.15
2.14
OUTPUT
2.423081
2.423081
2.423081
2.167738
2.167738
2.167738
2.052708
2.052708
2.052708
ERROR
0.016108
0.016108
0.009393
0.010458
0.012603
0.006767
0.005974
0.009466
0.00762
TABLE B.5
OUTPUT DATA FROM NEURAL NETWORK FOR CONVENTIONAL WITH LEAST RMS
ERROR VALUE (OUTPUT FOR 2 HIDDEN LAYERS -5 NODES. RMS ERROR= 0.090178)
INPUT
0.95
0.9
0.92
0.75
0.8
0.78
0.78
0.81
0.8
OUTPUT
0.891971
0.891971
0.891971
0.728379
0.728379
0.728379
0.65496
0.65496
0.65496
86
ERROR
0.003367
6.45E-05
0.000786
0.000467
0.00513
0.002665
0.015635
0.024037
0.021037
TABLE B.6
OUTPUT DATA FROM NEURAL NETWORK FOR ST1257B WITH LEAST RMS ERROR
VALUE (OUTPUT FOR 2 HIDDEN LAYERS -5 NODES. RMS ERROR= 0.115093)
INPUT
2.39
2.35
2.37
2.03
2.01
1.99
1.89
1.85
1.89
OUTPUT
2.353373
2.353373
2.353373
1.902937
1.902937
1.902937
1.712011
1.712011
1.712011
ERROR
0.001342
1.14E-05
0.000276
0.016145
0.011462
0.00758
0.03168
0.019041
0.03168
TABLE B.7
OUTPUT DATA FROM NEURAL NETWORK FOR ST1255G WITH LEAST RMS ERROR
VALUE (OUTPUT FOR 2 HIDDEN LAYERS -5 NODES. RMS ERROR= 0.146705)
INPUT
1.65
1.64
1.65
1.28
1.25
1.25
1.15
1.12
1.15
OUTPUT
1.430033
1.430033
1.430033
1.165676
1.165676
1.165676
1.048799
1.048799
1.048799
87
ERROR
0.048385
0.044086
0.048385
0.01307
0.007111
0.007111
0.010242
0.00507
0.010242
APPENDIX C
PICTORIAL REPRESENTATION OF DRILLED SURFACES
Drilled Surface Picture of SCFL material
Figure C.1. 5,000 rpm, 0.001 ipr,
Surface Finish : 1.1 µm
Figure C.2. 5,000 rpm, 0.01 ipr,
Surface Finish : 1.72 µm
Figure C.3. 2,500 rpm, 0.002 ipr,
Surface Finish : 1.18 µm
Figure C.4. 2,500 rpm, 0.008 ipr,
Surface Finish : 1.59 µm
Drilled Surface Pictures of Conventional Drill Bit
Figure C.5. 3,000 rpm, 0.004 ipr,
Surface Finish : 0.59 µm
Figure C.6. 3,000 rpm, 0.01 ipr,
Surface Finish : 0.99 µm
88
Drilled Surface Pictures of Brad spur Drill Bit
Figure C.7. 2,000 rpm, 0.004 ipr,
Surface Finish : 1.05 µm
Figure C.8. 2,000 rpm, 0.01 ipr,
Surface Finish : 1.49 µm
Drilled Surface Pictures of Double Margin Drill Bit
Figure C.9. 3,000 rpm, 0.004 ipr,
Surface Finish : 2.00 µm
Figure C.10. 3,000 rpm, 0.01 ipr,
Surface Finish : 2.53 µm
Drilled Surface Pictures of ST1257B Drill Bit
Figure C.11. 2,000 rpm, 0.004 ipr,
Surface Finish : 1.65 µm
Figure C.12. 2,000 rpm, 0.01 ipr,
Surface Finish : 2.05 µm
89
Drilled Surface Pictures of ST1255G Drill Bit
Figure C.13. 3,000 rpm, 0.004 ipr,
Surface Finish : 0.98 µm
Figure C.14. 3,000 rpm, 0.01 ipr,
Surface Finish : 1.55 µm
Magnified Pictures Showing Fiber Pullout and Zone Damage
Figure C.15. Fiber pullout
Figure C.16. Damaged zone
90
Figure C.17. Fiber pullout
Figure C.18. Magnified damage zone
91